Introducing a novel meta-heuristic optimization algorithm, the Flood Algorithm (FLA) draws inspiration from the intricate movement and flow patterns of water masses during flooding events in river basins. FLA mathemat...
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Introducing a novel meta-heuristic optimization algorithm, the Flood Algorithm (FLA) draws inspiration from the intricate movement and flow patterns of water masses during flooding events in river basins. FLA mathematically models key phenomena such as the movement of water toward slopes, flow rates over time, soil permeability effects, and periodic increases and decreases in water levels from precipitation and losses. Leveraging these models, the algorithm guides the movement and evolution of a population of potential solutions toward enhanced optimality. The algorithm endeavors to establish an appropriate correlation between the fundamental aspects of natural flood events and the optimization process. Its formulation and working mechanism are described in detail. It operates in two main phases-a regular movement phase, where the population moves naturally toward current best solutions, and a flooding phase, which introduces random disturbances to increase diversity. New solutions are periodically introduced while weaker ones are removed, mirroring the natural cycles of water levels. FLA's effectiveness is demonstrated through its application on well-known benchmark optimization problems and engineering design problems. Extensive comparisons have been carried out on CEC2005 functions using 16 algorithms in both basic and enhanced modes, as well as on CEC2014 functions with dimensions 30, 50, and 100 using a total of 20 other algorithms. These rigorous studies unequivocally confirm the robustness and strength of the proposed algorithm. Furthermore, the algorithm's performance on 12 constrained engineering problems demonstrates its ability to tackle real-world challenges. The FLA's source code is publicly available at https://***/projects/fla.
The first aim of the development of compact spectrometers was to bring the lab measurements to the field or into the process line. This has been accomplished in many industrial applications - optical characterization,...
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ISBN:
(数字)9781510642225
ISBN:
(纸本)9781510642225
The first aim of the development of compact spectrometers was to bring the lab measurements to the field or into the process line. This has been accomplished in many industrial applications - optical characterization, pharmaceutics, biotechnology, chemistry etc. - by maintaining the required performance level. It was at the time when miniature spectrometers emerged (size <10000 cm(3) in the study). A few years later, the same spectrometers also opened up new applications where spectroscopy had not been used before: precision farming, recycling, process control, etc. The range of potential applications became vast. The market started to be split into a wide variety of niche adoptions, each having its own requirements (performance, costs, design, operating conditions, ...). This forced the development of products specific to each segment. At the same time, micro spectrometers were first presented (size between 10000 cm(3) and 100 cm(3)), offering a similar performance to miniature devices but in a handheld, portable design. They enabled the launch of new systems for professional users. At the moment, it is still research and industrial optical characterization that possess the biggest shares within the compact spectrometer market. However, the better knowledge of end-users needs results in improving medium series applications (agriculture, environment) and also in developing further solutions for professionals (hair analyzers, textile identifiers, cannabis testers etc.). A turning point for the market is coming It is the arrival of global leaders, both in the role of manufacturers (AMS Technologies, Osram, Viavi Solutions) and end users (Huawei, Samsung, Bosch, Henkel). Big players will drive the market towards consumer and biomedical applications (image enhancement, personal monitoring etc.). This is possible due to the emergence of chip size spectral sensors (<1 cm(3)).
BACKGROUND: An isolated robot must take account of uncertainty in its world model and adapt its activities to take into account such as uncertainty. In the same way, a robot interaction with security and privacy issue...
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BACKGROUND: An isolated robot must take account of uncertainty in its world model and adapt its activities to take into account such as uncertainty. In the same way, a robot interaction with security and privacy issues (RISAPI) with people has to account for its confusion about the human internal state, as well as how this state will shift as humans respond to the robot. OBJECTIVES: This paper discusses RISAPI of our original work in the field, which shows how probabilistic planning and system theory algorithms in workplace robotic systems that work with people can allow for that reasoning using a security robot system. The problem is a general way as an incomplete knowledge 2-player game. RESULTS: In this general framework, the various hypotheses and these contribute to thrilling and complex robot behavior through real-time interaction, which transforms actual human subjects into a spectrum of production systems, robots, and care facilities. CONCLUSION: The models of the internal human situation, in which robots can be designed efficiently, are limited, and achieve optimal computational intractability in large, high-dimensional spaces. To achieve this, versatile, lightweight portrayals of the human inner state and modern algorithms offer great hope for reasoning.
Much of econometrics is based on a tight probabilistic approach to empirical modeling that dates back to Haavelmo (1944). This thesis explores a modern algorithmic view, and by doing so, finds solutions to classic pro...
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Much of econometrics is based on a tight probabilistic approach to empirical modeling that dates back to Haavelmo (1944). This thesis explores a modern algorithmic view, and by doing so, finds solutions to classic problems while developing new avenues. In the first chapter, Kalman-filter based computations of random walk coefficients are replaced by a closed-form solution only second to least squares in the pantheon of simplicity. In the second chapter, random walk “drifting” coefficients are themselves dismissed. Rather, evolving coefficients are modeled and forecasted with a powerful machine learning algorithm. Conveniently, this generalization of time-varying parameters provides statistical efficiency and interpretability, which off-the-shelf machine learning algorithms cannot easily offer. The third chapter is about the to the fundamental problem of detecting at which point a learner stops learning and starts imitating. It answers “why can’t Random Forest overfit?” The phenomenon is shown to be a surprising byproduct of randomized “greedy” algorithms – often deployed in the face of computational adversity. Then, the insights are utilized to develop new high-performing non-overfitting algorithms.
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