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With additive manufacturing (AM) processes such as Wire Arc Additive Manufacturing (WAAM), components with complex shapes or with functional properties can be produced, with advantages in the areas of resource conservation, lightweight construction, and load-optimized production. However, proving component quality is a challenge because it is not possible to produce 100% defect-free components. In addition to this, statistically determined fluctuations in the wire quality, gas flow, and their interaction with process parameters result in a quality of the components that is not 100% reproducible. Complex testing procedures are therefore required to demonstrate the quality of the components, which are not cost-effective and lead to less efficiency. As part of the project "3DPrintFEM", a sound emission analysis is used to evaluate the quality of AM components. Within the scope of the project, an approach was being developed to determine the quality of an AM part dependent not necessarily on its geometry. Samples were produced from WAAM, which were later cut and milled to precision. To determine the frequencies, the samples were put through a resonant frequency test (RFM). The unwanted modes were then removed from the spectrum produced by the experiments by comparing it with FEM simulations. Later, defects were introduced in experimental samples in compliance with the ISO 5817 guidelines. In order to create a database of frequencies related to the degree of the sample defect, they were subjected to RFM. The database was further augmented through frequencies from simulations performed on samples with similar geometries, and, hence, a training set was generated for an algorithm. A machine-learning algorithm based on regression modelling was trained based on the database to sort samples according to the degree of flaws in them. The algorithm's detectability was evaluated using samples that had a known level of flaws which forms the test dataset. Based on the outcome, the algorithm will be integrated into an equipment developed in-house to monitor the quality of samples produced, thereby having an in-house quality assessment routine. The equipment shall be less expensive than conventional acoustic equipment, thus helping the industry cut costs when validating the quality of their components. [ABSTRACT FROM AUTHOR]

The modular multilevel converter (MMC) has many advantages of low switching losses, good harmonic performance and high modularity structure in state-of-the-art HVDC applications. The full-bridge submodules (FBSMs) of the hybrid MMC can inherently output negative voltage to absorb fault currents, and consequently the hybrid MMC can ride through severe DC faults without blocking. During the DC fault-ride-through process, the submodule capacitor voltage and arm current of the MMC will be temporarily increased. These characteristics limit the proportion of the FBSMs, which should not be too low and thus increase the cost and operating losses of the hybrid MMC. In this paper, an improved sorting algorithm of SM capacitor voltage is established, and a novel virtual damping control strategy is proposed that can effectively suppress the increase in submodule capacitor voltage and arm current of the hybrid MMC during the DC fault-ride-through process. By adopting this optimization control, the proportion of FBSMs can be reduced significantly without deteriorating the fault-ride-through capability or safety of the MMC. The effectiveness of the proposed control is verified by careful theoretical analysis and detailed simulation results. [ABSTRACT FROM AUTHOR]

BACKGROUND: The gangue content in coal seriously affects the calorific value produced by its combustion. In practical applications, gangue in coal needs to be completely separated. The pseudo-dual-energy X-ray method does not have high sorting accuracy. OBJECTIVE: This study aims to propose a novel multi-dimensional coal and gangue X-ray sorting algorithm based on CdZnTe photon counting detectors to solve the problem of coal and gangue sorting by X-ray. METHODS: This complete algorithm includes five steps: (1) Preferred energy bins, (2) transmittance sorting, (3) one-dimensional R-value sorting, (4) two-dimensional R-value sorting, and (5) three-dimensional R-value sorting. The output range of each step is determined by prior information from 65 groups of coal and gangue. An additional 110 groups of coal and gangue are employed experimentally to validate the algorithm's accuracy. RESULTS: Compared with the 60% sorting accuracy of the Pseudo-dual-energy method, the new algorithm reached a sorting accuracy of 99%. CONCLUSIONS: Study results demonstrate the superiority of this novel algorithm and its feasibility in practical applications. This novel algorithm can guide other two-substance X-ray sorting applications based on photon counting detectors. [ABSTRACT FROM AUTHOR]

Finding methods to teach sorting algorithms is a challenge for most Computer Science teachers, but also for students who are open to learn and search for information. Because nowadays students are very receptive to educational software, we want to introduce our instrument named ALSort – a software designed for teachers and students interested in the instructional process of sorting algorithms. ALSort is an educational software based on gamification and modelling. It is built on six stages of challenges related to the six levels of learning from the Revised Bloom Taxonomy and the version from this paper is a prototype. [ABSTRACT FROM AUTHOR]

• A new event-driven sorting algorithm-based merging Monte Carlo (SAMMC) method is developed for solving aerosol dynamics. • Neighbour merging method is proposed to maintain a constant-number and constant-volume scheme, and population information. • Both absolute volume difference (AVD) and relative volume difference (RVD) are used as the crucial merging criteria. • The stochastic errors and computational time of SAMMC method are quantitatively compared with previous MC methods. • The AVD- and RVD-based SAMMC methods show very high computational accuracy and efficiency in all aerosol dynamic processes. A new event-driven sorting algorithm-based merging Monte Carlo (SAMMC) method is proposed and developed for solving the general dynamic equation in aerosol dynamics. A neighbour merging method is proposed to maintain a constant-volume and constant-number scheme with minimal interference to the numerical particle population, where absolute volume difference (AVD) and relative volume difference (RVD) are used as the crucial merging criteria. The SAMMC method can be used for simulating all aerosol dynamic processes with very high computational accuracy, especially effective in those aerosol dynamic processes generating additional numerical particles. In the present study, comprehensive computational conditions are used to study their impacts on computational accuracy and efficiency by comparing the SAMMC method to previous MC methods and analytical solutions. Numerical results show that the SAMMC method has excellent agreement with analytical solutions for all specified cases of different aerosol dynamic processes and shows higher computational accuracy than equal-weight-based MC methods. In addition, the computational accuracy of the SAMMC method in the total particle number concentration is much higher than those of the weighted fraction Monte Carlo (WFMC) method and sorting algorithm-based merging weighted fraction Monte Carlo (SAMWFMC) method in non-homogeneous coagulation. The SAMMC method can also achieve the same computational precision as the multi-Monte Carlo (MMC) method at only slightly higher computational cost in homogeneous coagulation. More importantly, the SAMMC method can deal with breakage-related processes and simultaneous coagulation and nucleation with very high computational accuracy and efficiency, while the numerical results of the MMC method may significantly deviate from analytical solutions due to the introduction of systematic errors. Furthermore, the RVD can achieve higher computational accuracy in multi-breakage modelling than AVD, but AVD and RVD have almost the same computational efficiency and accuracy in other aerosol dynamic processes. [ABSTRACT FROM AUTHOR]

The order-of-addition (OofA) experiment has received a great deal of attention in the recent literature. The primary goal of the OofA experiment is to identify the optimal order in a sequence of m components. All the existing methods are model-dependent and are limited to small number of components. The appropriateness of the resulting optimal order heavily depends on (a) the correctness of the underlying assumed model, and (b) the goodness of model fitting. Moreover, these methods are not applicable to deal with large m (e.g., m ≥ 7 ). With this in mind, this article proposes an efficient adaptive methodology, building upon the quick-sort algorithm, to explore the optimal order without any model specification. Compared to the existing work, the run sizes of the proposed method needed to achieve the optimal order are much smaller. Theoretical supports are given to illustrate the effectiveness of the proposed method. The proposed method is able to obtain the optimal order for large m (e.g., m ≥ 20 ). Numerical experiments are used to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

For defective drilled lotus seeds, the inner bitter lotus plumule cannot be removed normally, leading to difficulties in subsequent nutrient extraction and food processing. There is an obvious difference in visibility of drilled hole between normal and defective drilled lotus seeds in the top view; thus, an online sorting method for drilled lotus seeds based on drilled hole detection is proposed in this study. First, a drilled hole detection model based on You Only Look Once (YOLOv3) is developed to detect the drilled hole features on the lotus seed surface. The model was tested and compared with the Faster Region-based Convolutional Neural Network (Faster R-CNN) and Single Shot MultiBox Detector (SSD) models, and it showed a better comprehensive performance in terms of accuracy and speed. A sorting control algorithm is also proposed to perform online sorting based on real-time drilled hole detection results. In addition, an auxiliary algorithm is proposed to prevent the boundary misjudgement of detected hole ownership between adjacent lotus seeds during continuous sorting. An online sorting system was designed, and sorting tests were performed. A sorting accuracy of 95.8% was achieved for the mixed defective and normal drilled lotus seed samples. The proposed method is expected to not only fulfil the practical requirements for the online sorting of drilled lotus seeds but also provide references for other agricultural products that require continuous online sorting based on the detection of local features. • Drilled lotus seeds can be sorted based on the detection of drilled hole. • An online sorting system for drilled lotus seeds was proposed. • A drilled hole detection model was developed based on YOLOv3. • The proposed sorting control algorithm can achieve online continuous sorting. • A sorting accuracy rate of 95.8% was achieved using the developed sorting system. [ABSTRACT FROM AUTHOR]

Featured Application: This study is useful for a series-integrated separation between good parts and defective ones via acoustic resonance testing. The work primarily relies on the simulation-based generation of mandatory and application-specific training data for defect detection. It also considers an optional use of additional structural component information in data evaluation. The given achievements may enable a time- and cost-optimized training procedure, allow for applying acoustic resonance testing despite a limited number of representative training parts, or provide a way to counter significant part-to-part variations for improved defect detection. Analyzing eigenfrequencies by acoustic resonance testing enables a fast screening of components regarding structural defects. The eigenfrequencies of each specific part depend on the general geometric and material properties, including tolerable part-to-part variations, as well as on possible structural flaws. Separating good parts from defective ones is not straightforward and each application-specific sorting algorithm is usually found from experimental training data. However, there are limitations and training data collection may be intricate. We worked on this challenge focusing on machine-made model parts varying slightly in geometry. The application objective was the eigenfrequency-based detection of parts featuring a through-hole test defect drilled into some of the parts and enlarged stepwise. The eigenfrequencies were measured concomitantly. Unlike the industry standard, our approach is based on synthetic training data created mainly by simulation techniques, which resulted in a principally satisfactory classification of the good and defective parts. However, the parts with small defects were not identified from the eigenfrequencies alone, due to overlaying geometric variations. In order to counteract such noise and to improve defect detection based on synthetic training data, the specific actual part geometry was used, in the sense of additional a priori information. A multimodal data evaluation model showed a clearly enhanced sorting power. [ABSTRACT FROM AUTHOR]

The manufacturing of solderless wrapped connections in traceable and reproducible high quality requires an automated manufacturing process. However, one key challenge for robot-based wiring is the definition of wiring sequence, as the proximity of the array-oriented wrapposts exacerbates tool accessibility. In this research, we optimize the wiring sequence by treating it as a constraint satisfaction problem, whereby a solution is indispensable, even if the constraints are not fulfilled. Experiments on a pilot plant for robot-based manufacturing of wrapped connections show that the percentage of automatically wired connections can be significantly increased with the developed approach. [ABSTRACT FROM AUTHOR]

Following the footprints of what has been done with other sorting devices, we study a popqueue and define an optimal sorting algorithm, called Cons. Our results include a description of the set of all the preimages of a given permutation, an enumeration of the set of the preimages of permutations with some specific properties and, finally, the exact enumeration of permutations having 0, 1 and 2 preimages, respectively, with a characterization of permutations having 3 preimages. [ABSTRACT FROM AUTHOR]

Non-volatile memory (NVM) has emerged as an alternative of the next-generation memory due to its non-volatility, byte addressability, high storage-density, and low-energy consumption. However, NVM also has some limitations, e.g. asymmetric read and write latency. Therefore, at present, it is not realistic to completely replace DRAM with NVM in computer systems. A more feasible scheme is to adopt the hybrid memory architecture composed of NVM and DRAM. Following the assumption of hybrid memory architecture, in this paper, we propose an NVM-friendly sorting algorithm called NVMSorting. Particularly, we introduce a new concept called Natural Run to improve the existing MONTRES algorithm. Further, we apply the proposed NVMSorting to database join algorithms to improve the performance of the existing sort-merge join. To verify the performance of our proposal, we implement six existing sorting algorithms as baselines, including the MONTRES algorithm, and conduct comparative experiments on real Intel Optane DC persistent memory. The results show that NVMSorting outperforms other sorting algorithms in terms of execution time and NVM writes. In addition, the results of the join experiment show that the NVMSorting algorithm achieves the highest performance among all schemes. Especially, in the partially ordered data, the execution time of NVMSorting is 2.9%, 2.7%, and 4.2% less than MONTRES, external sort, and quick sort, respectively. Also, the amount of NVM writes of the NVMSorting is 26.1%, 43.6%, 96.2% less than MONTRES, external sort, and quick sort, respectively. [ABSTRACT FROM AUTHOR]

Analyzing eigenfrequencies by acoustic resonance testing enables a fast screening of components regarding structural defects. The eigenfrequencies of each specific part depend on the general geometric and material properties, including tolerable part-to-part variations, as well as on possible structural flaws. Separating good parts from defective ones is not straightforward and each application-specific sorting algorithm is usually found from experimental training data. However, there are limitations and training data collection may be intricate. We worked on this challenge focusing on machine-made model parts varying slightly in geometry. The application objective was the eigenfrequency-based detection of parts featuring a through-hole test defect drilled into some of the parts and enlarged stepwise. The eigenfrequencies were measured concomitantly. Unlike the industry standard, our approach is based on synthetic training data created mainly by simulation techniques, which resulted in a principally satisfactory classification of the good and defective parts. However, the parts with small defects were not identified from the eigenfrequencies alone, due to overlaying geometric variations. In order to counteract such noise and to improve defect detection based on synthetic training data, the specific actual part geometry was used, in the sense of additional a priori information. A multimodal data evaluation model showed a clearly enhanced sorting power.

• An optimized sorting algorithm is proposed to remove deadlock without searching for global dependent cycles. • The effective face method is applied in the spatial discretization to deal with the integrals on the non-planar cell faces. • The convergence of the splitting iterative algorithm is proved rigorously. • The new method is verified to be conducive to the convergence of the iteration by numerical experiments. • Numerical results show the effectiveness and high efficiency of the new algorithm on general hexahedral meshes. We propose a new sweeping solution method for the three-dimensional (3D) discrete ordinates (Sn) equations of neutron transport on general hexahedral meshes, with particular focus on handling the surface integrals and sweeping deadlocks. The main contributions of this paper include three aspects. Firstly, the surface integrals on the non-planar cell faces are well discretized by virtue of the effective face method, which successfully addresses the reentrance problem without the decomposition of the cell faces. Secondly, based on the effective face method, we devise a new sorting algorithm which works for any hexahedral meshes. In this algorithm, the identification for dependent cycles is avoided and the physical characteristics of transport problems are taken into account in the choice of lagged cells used to decouple the sweeping deadlocks. Combining the above advancements, a splitting sweeping iterative method is proposed for the solution of the Sn equations on general hexahedral meshes. Finally, we prove theoretically that this sweeping iterative method always converges, and the decoupling method affects little on iterative convergence rate. Numerical experiments are presented to demonstrate the effectiveness of the proposed methods on both cubic and spherical domains. The ideas presented in this paper are also applicable to the polyhedral meshes or two-dimensional polygonal meshes. [ABSTRACT FROM AUTHOR]

Following the footprints of what has been done with the algorithm Stacksort, we investigate the preimages of the map associated with a slightly less well known algorithm, called Queuesort. After having described an equivalent version of Queuesort, we provide a recursive description of the set of all preimages of a given permutation, which can be also translated into a recursive procedure to effectively find such preimages. We then deal with some enumerative issues. More specifically, we investigate the cardinality of the set of preimages of a given permutation, showing that all cardinalities are possible, except for 3. We also give exact enumeration results for the number of permutations having 0,1 and 2 preimages. Finally, we consider the special case of those permutations π whose set of left-to-right maxima is the disjoint union of a prefix and a suffix of π : we determine a closed formula for the number of preimages of such permutations, which involves two different incarnations of ballot numbers, and we show that our formula can be expressed as a linear combination of Catalan numbers. [ABSTRACT FROM AUTHOR]

Emerging persistent memory (PM, also termed as non-volatile memory) technologies can promise large capacity, non-volatility, byte-addressability and DRAM-comparable access latency. A host of PM-based storage systems and applications that store and access data directly in PM have been inspired by such amazing features. Sorting is an important function for many systems, but how to optimize sorting for PM-based systems has not been systematically studied. In this paper, we conduct extensive experiments for many existing sorting methods, including both conventional sorting algorithms adapted for PM and recently-proposed PM-friendly sorting techniques, on a DRAM and real PM hybrid platform. The results indicate that these sorting methods all have drawbacks for various workloads. Some of the results are even counterintuitive compared to running on a DRAM-simulated platform in their papers. To the best of our knowledge, we are the first to perform a systematic study on the sorting issue for persistent memory. Based on our study, we summarize principles on selecting the optimal algorithms and propose an adaptive sorting engine called PMSort to perform the selecting operation and reduce failure recovery overhead in PM. We conduct extensive experiments and show that PMSort can select the best sorting algorithm in a variety of cases. [ABSTRACT FROM AUTHOR]