Background: Insufficient sleep in children associates with adverse health outcomes. Evidence in young children shows waist placement may be superior to the wrist to estimate sleep duration (SDu). ...Little is known about the accuracy of the Sadeh (S) and Cole-Kripke (CK) algorithms measuring SDu in children. The purpose of this study was to compare SDu measured with wrist and waist accelerometry using the S and CK algorithms in 8- to 10-year-old children in reference to parental reports of SDu (PSD). An exploratory aim was to evaluate if obesity status modified these associations. Methods: Children (n = 45) were classified as having normal weight (NW = 22) or excessive weight (EW = 23) if their BMI percentile fell above or below 85. Children simultaneously wore a GT9X Actigraph accelerometer on the waist and non-dominant wrist. Parents documented timepoints for when the child went to sleep and woke up each day. Pearson correlation coefficients measured SDu estimates using the S and CK algorithms by site placement against PSD. Results: Age and sex distribution did not differ between groups. The mean PSD was 9.7 ± 0.6 hours. SDu by S-wrist was 6.1 ± 1.8 hours (r = 0.42, p = 0.0056), S-waist: 9.3 ± 0.8 hours (r = 0.73, p < 0.0001), CKwrist: 8.1 ± 1.2 hours (r = 0.43, p = 0.0033), and CK-waist: 9.7 ± 0.8 hours (r = 0.60, p < 0.0001). In children with EW, SDu measured with S-waist (r = 0.75, p < 0.0001) and CK-waist (r = 0.74, p < 0.0001) algorithms similarly correlated with PSD. In NW, S-waist strongest correlated with PSD (r = 0.76, p < 0.0001), while CK-waist had a lower correlation coefficient (r = 0.53, p = 0.0112) compared to EW. The lowest correlations were obtained with wrist actigraphy (data not shown). Conclusions: Our preliminary results suggest that waist accelerometry and the S algorithm may be better than wrist accelerometry and the CK-algorithm to estimate sleep duration in 8- to 10-year-old children with NW or EW.
Evolutionary algorithms have shown their promise in coping with many-objective optimization problems. However, the strategies of balancing convergence and diversity and the effectiveness of handling ...problems with irregular Pareto fronts (PFs) are still far from perfect. To address these issues, this paper proposes an adaptive sorting-based evolutionary algorithm based on the idea of decomposition. First, we propose an adaptive sorting-based environmental selection strategy. Solutions in each subpopulation (partitioned by reference vectors) are sorted based on their convergence. Those with better convergence are further sorted based on their diversity, then being selected according to their sorting levels. Second, we provide an adaptive promising subpopulation sorting-based environmental selection strategy for problems which may have irregular PFs. This strategy provides additional sorting-based selection effort on promising subpopulations after the general environmental selection process. Third, we extend the algorithm to handle constraints. Finally, we conduct an extensive experimental study on the proposed algorithm by comparing with start-of-the-state algorithms. Results demonstrate the superiority of the proposed algorithm.
Remora optimization algorithm Jia, Heming; Peng, Xiaoxu; Lang, Chunbo
Expert systems with applications,
12/2021, Letnik:
185
Journal Article
Recenzirano
In this paper, Remora Optimization Algorithm (ROA) is proposed, which is a new bionics-based, natural-inspired, and meta-heuristic algorithm. The inspiration for ROA is mainly due to the parasitic ...behavior of remora. Different locations are updated in different hosts: In some large hosts, remora feeds on the host's ectoparasites or wreckage and evades natural enemies, for example in the case of giant whales. In some small hosts, remora follows the host to move to the bait-rich area to prey, taking the fast-moving swordfish as an example. In the case of these two update methods, remora also makes some judges based on experience. If it takes the initiative to prey, it updates the host, makes a global update. If it eat around the host, remora does not change the host, and continues to local update. This algorithm is more inclined to provide a new idea for memetic algorithm, because the host in ROA can be reasonably replaced, such as ships, turtles, etc. The above dynamic mode and behavior are simulated mathematically and the validity of the ROA is tested with 29 benchmark questions and 5 actual engineering questions. Parallel comparisons are made with 10 other natural heuristics. The statistical results and comparisons show that ROA provides a very promising prospect and a strong competitive ability compared to other state-of-the-art heuristic techniques.
Data clustering is the process of identifying natural groupings or clusters based on a certain similarity measure in muti-dimensional data. Aiming at the dynamic clustering problem where the number ...of clusters cannot be determined in advance, a hybrid dynamic clustering method based on the marine predators algorithm (MPA) and particle swarm optimization (PSO) algorithm was proposed. The position update strategy of the PSO algorithm was used to make up for the lack of MPA in global searching. The fixed-length coding strategy with the real number coding method was used to deal with the variable length clustering optimization problem, and the unfeasible solution processing strategy and the penalty function strategy are adopted to improve the performance of the algorithm and achieve simultaneous optimization of the number of clusters and cluster centers. The proposed MPA-PSO algorithm with PSO algorithm, MPA, Differential Evolution (DE) algorithm, Spotted Hyena Optimizer (SHO), Lightning Searching Algorithm (LSA) and Equilibrium Optimizer (EO) are adopted to carry out the clustering simulation experiments on four artificial data sets and six real data sets (Iris, Wine, Wisconsin breast cancer, Vowel, Seeds, and Wdbc) in UCI databases. Three performance indicators (the number of clusters, ARI and Accuracy) are used to evaluate the clustering results. The experimental results show that the proposed method can not only successfully find the correct number of clusters, but also obtain stable results for most test problems.
This paper proposes a Sine Cosine hybrid optimization algorithm with Modified Whale Optimization Algorithm (SCMWOA). The goal is to leverage the strengths of WOA and SCA to solve problems with ...continuous and binary decision variables. The SCMWOA algorithm is first tested on nineteen datasets from the UCI Machine Learning Repository with different numbers of attributes, instances, and classes for feature selection. It is then employed to solve several benchmark functions and classical engineering case studies. The SCMWOA algorithm is applied for solving constrained optimization problems. The two tested examples are the welded beam design and the tension/compression spring design. The results emphasize that the SCMWOA algorithm outperforms several comparative optimization algorithms and provides better accuracy compared to other algorithms. The statistical analysis tests, including one-way analysis of variance (ANOVA) and Wilcoxon's rank-sum, confirm that the SCMWOA algorithm performs better.
Modern signal processing (SP) methods rely very heavily on probability and statistics to solve challenging SP problems. SP methods are now expected to deal with ever more complex models, requiring ...ever more sophisticated computational inference techniques. This has driven the development of statistical SP methods based on stochastic simulation and optimization. Stochastic simulation and optimization algorithms are computationally intensive tools for performing statistical inference in models that are analytically intractable and beyond the scope of deterministic inference methods. They have been recently successfully applied to many difficult problems involving complex statistical models and sophisticated (often Bayesian) statistical inference techniques. This survey paper offers an introduction to stochastic simulation and optimization methods in signal and image processing. The paper addresses a variety of high-dimensional Markov chain Monte Carlo (MCMC) methods as well as deterministic surrogate methods, such as variational Bayes, the Bethe approach, belief and expectation propagation and approximate message passing algorithms. It also discusses a range of optimization methods that have been adopted to solve stochastic problems, as well as stochastic methods for deterministic optimization. Subsequently, areas of overlap between simulation and optimization, in particular optimization-within-MCMC and MCMC-driven optimization are discussed.
AGATA—Advanced GAmma Tracking Array Akkoyun, S.; de Angelis, G.; Arnold, L. ...
Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment,
2012, Letnik:
668
Journal Article
Recenzirano
Odprti dostop
The Advanced GAmma Tracking Array (AGATA) is a European project to develop and operate the next generation γ-ray spectrometer. AGATA is based on the technique of γ-ray energy tracking in electrically ...segmented high-purity germanium crystals. This technique requires the accurate determination of the energy, time and position of every interaction as a γ ray deposits its energy within the detector volume. Reconstruction of the full interaction path results in a detector with very high efficiency and excellent spectral response. The realisation of γ-ray tracking and AGATA is a result of many technical advances. These include the development of encapsulated highly segmented germanium detectors assembled in a triple cluster detector cryostat, an electronics system with fast digital sampling and a data acquisition system to process the data at a high rate. The full characterisation of the crystals was measured and compared with detector-response simulations. This enabled pulse-shape analysis algorithms, to extract energy, time and position, to be employed. In addition, tracking algorithms for event reconstruction were developed. The first phase of AGATA is now complete and operational in its first physics campaign. In the future AGATA will be moved between laboratories in Europe and operated in a series of campaigns to take advantage of the different beams and facilities available to maximise its science output. The paper reviews all the achievements made in the AGATA project including all the necessary infrastructure to operate and support the spectrometer.
Reconfigurable intelligent surfaces (RISs) have been recently considered as a promising candidate for energy-efficient solutions in future wireless networks. Their dynamic and low-power configuration ...enables coverage extension, massive connectivity, and low-latency communications. Due to a large number of unknown variables referring to the RIS unit elements and the transmitted signals, channel estimation and signal recovery in RIS-based systems are the ones of the most critical technical challenges. To address this problem, we focus on the RIS-assisted wireless communication system and present two joint channel estimation and signal recovery schemes based on message passing algorithms in this paper. Specifically, the proposed bidirectional scheme applies the Taylor series expansion and Gaussian approximation to simplify the sum-product procedure in the formulated problem. In addition, the inner iteration that adopts two variants of approximate message passing algorithms is incorporated to ensure robustness and convergence. Two ambiguities removal methods are also discussed in this paper. Our simulation results show that the proposed schemes show the superiority over the state-of-art benchmark method. We also provide insights on the impact of different RIS parameter settings on the proposed schemes.
•Modification of Bat algorithm for optimum design of TMD.•Optimization of all TMD parameters such as mass, stiffness and damping coefficient.•Bat Algorithm merged with iterative dynamic analyses of ...structures.•An effective, fast and reliable methodology comparing to existing ones.
Metaheuristic algorithms are effective for optimization with diverse applications in engineering. The optimum tuning of tuned mass dampers is very important for seismic structures excited by random vibrations, and optimization techniques have been used to obtain the best performance for optimally tuned mass dampers. In this study, a novel optimization approach employing the bat algorithm with several modifications for the tuned mass damper optimization problem is presented. In the proposed method, the design variables such as the mass, period and damping ratio of tuned mass damper are optimized and different earthquake records are considered during the optimization process. The method is then applied to a ten-story civil structure and the results are then compared with the analytical methods and other methods such as genetic algorithms, particle swarm optimization, and harmony search. The comparison shows that the proposed method is more effective than other compared methods. Additionally, the robustness of the optimum results was evaluated. The proposed approach for optimizating tuned mass dampers via the bat algorithm is a feasible and efficient approach.
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