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[Laser Institute of America ICALEO? 2015: 34th International Congress on Laser Materials Processing, Laser Microprocessing and Nanomanufacturing - Atlanta, Georgia, USA (October 18–22, 2015)] International Congress on Applications of Lasers & Electro-Optics - Prediction of weld bead for fiber laser keyhole welding based on FEA
摘要： Fiber laser keyhole welding as a popular metal joining process has been widely used in a variety of applications especially automotive, shipbuilding and aerospace industries. Although process parameters determination based on experiments is the frequently used in the practical welding, it is often a very costly and time consuming. Accurately predicting the weld bead without expensive trial experiments has great theoretical significance and engineering value for welding process parameters pre-selection. An innovative volume heat source model was proposed for weld bead geometry prediction through finite element analysis (FEA) in fiber laser keyhole welding. The hybrid heat source model consists of a double ellipsoid heat source and a 3D Gaussian heat distribution model. To validate the effectiveness of the proposed heat source model, the fiber laser keyhole welding of the stainless steel SUS301L-HT has been carried out in this paper. The main three parameters, laser power (LP), welding speed (WS) and focal position (FP) have been taken into consideration as the design variables. Both of the predicted values from the FEA and back propagation neural network (BPNN) are compared with the experimental results. The FEA predicted results achieve good agreement with experimental results of weld bead shape and dimension and are better than BPNN predicted results. The objective variation trend is also analyzed by two prediction methods. From the discussion, it is revealed that the proposed prediction method of weld bead is effective for fiber laser keyhole welding process and replacing the expensive experiments.
关键词： Weld bead prediction,Keyhole welding,Fiber laser,FEA,BPNN
更新于2025-09-12 10:27:22
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Laser-induced forward transfer of soft material nanolayers with millisecond pulses shows contact-based material deposition
摘要： In this work, we present a qualitative and quantitative experimental analysis, as well as a numerical model, of a novel variant of the laser-induced forward transfer, which uses millisecond laser pulses. In this process, soft material nanolayer spots are transferred from a donor slide, which is coated with the soft material layer, to an acceptor slide via laser irradiation. This method offers a highly flexible material transfer to perform high-throughput combinatorial chemistry for the generation of biomolecule arrays. For the first time, we show visual evidence that the main transfer mechanism is contact-based, due to thermal surface expansion of the donor layer. Thus, the process is different from the many known variants of laser-induced forward transfer. We will characterize the maximum axial surface expansion in relation to laser power and pulse duration. On this basis, we derive a numerical model that approximates the axial surface expansion within measurement tolerances. Finally, we analyze the topology of the transferred soft material nanolayer spots by fluorescence imaging and vertical scanning interferometry to determine width, height, and shape of the transferred material. Concluding from this experimental and numerical data, we can now predict the amount of transferred material in this process.
关键词： high-speed imaging,fluorescence imaging,experimental and numerical prediction,vertical scanning interferometry,OpenFOAM
更新于2025-09-12 10:27:22
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Assessment of Artificial Neural Networks Learning Algorithms and Training Datasets for Solar Photovoltaic Power Production Prediction
摘要： The capability of accurately predicting the Solar Photovoltaic (PV) power productions is crucial to effectively control and manage the electrical grid. In this regard, the objective of this work is to propose an efficient Artificial Neural Network (ANN) model in which 10 different learning algorithms (i.e., different in the way in which the adjustment on the ANN internal parameters is formulated to effectively map the inputs to the outputs) and 23 different training datasets (i.e., different combinations of the real-time weather variables and the PV power production data) are investigated for accurate 1 day-ahead power production predictions with short computational time. In particular, the correlations between different combinations of the historical wind speed, ambient temperature, global solar radiation, PV power productions, and the time stamp of the year are examined for developing an efficient solar PV power production prediction model. The investigation is carried out on a 231 kWac grid-connected solar PV system located in Jordan. An ANN that receives in input the whole historical weather variables and PV power productions, and the time stamp of the year accompanied with Levenberg-Marquardt (LM) learning algorithm is found to provide the most accurate predictions with less computational efforts. Specifically, an enhancement reaches up to 15, 1, and 5% for the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2) performance metrics, respectively, compared to the Persistence prediction model of literature.
关键词： learning algorithms,training datasets,solar photovoltaic,persistence,Artificial Neural Networks,power prediction
更新于2025-09-12 10:27:22
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A multi-mode excitation hardness prediction method based on Controlled Laser Air-Force Detection (CLAFD) technique
摘要： A novel material hardness testing method was proposed based on controlled laser air-force detection (CLAFD) technique. Polyurethane was chosen as the study object. Multi-mode excitation was adopted. Partial least square as the modeling method was used to build the hardness prediction model on the data of laser displacement. Different preprocessing methods were carried out for eliminating the noise of the original data. The results showed the multiplicative scattering model analysis for the data of stress relaxation mode. The results showed that the hardness could be predicted with high precision. The relationship coefficients of the prediction set (Rp) was above 0.90, and the residual prediction deviation (RPD) was more than 2. Furthermore, the Rp of the transient was 0.93, the RPD was 2.51, the excitation time was 1 s, showing that the transient mode performed with precision in high-speed hardness detection. The highest precision was based on the stress relaxation mode, so we did further study on the interval correction (MSC) had the best performance. Among four modes, the relationship coefficients of the prediction set (Rp) was up to 0.99, and the RPD was 3.54 when the time of the stress relaxation mode lasted 60 s. Based on the results above, the prediction ability would improve further when the relaxation time is increased. The study will provide a new real-time, non-destruction and cross-contamination free hardness detection method for material science, especially for those materials such as artificial biological tissue, function food products, etc.
关键词： Hardness prediction,Biological tissue,Cross-contamination-free,Multi-mode excitation,Polyurethane,Controlled laser air-force detection (CLAFD)
更新于2025-09-12 10:27:22
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[IEEE 2018 IEEE International Conference on Information and Automation (ICIA) - Wuyishan, China (2018.8.11-2018.8.13)] 2018 IEEE International Conference on Information and Automation (ICIA) - Research on Photovoltaic Power Generation Power Prediction Algorithm Based on Component Aging
摘要： The timely safety improvement of power system scheduling and the reasonable arrangement of various forms of power supply work are realized by the accurate photovoltaic power forecasting.Therefore, in order to achieve a more accurate forecast of photovoltaic power, In this paper, the photovoltaic power generation system based on the theory of the similar day output power of the defects in the prediction algorithm was improved, in the new algorithm consider the aging of components, so it is concluded that the power prediction more accurate.
关键词： Photovoltaic power generation,Prediction algorithm,Photovoltaic panel life cycle,Power prediction
更新于2025-09-11 14:15:04
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Ultrastretchable Hybrid Electrodes of Silver Nanowires and Multiwalled Carbon Nanotubes Realized by Capillary‐Force‐Induced Welding
摘要： Background: The International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) Injury Severity Score (ICISS) is a risk adjustment model when injuries are recorded using ICD-9-CM coding. The trauma mortality prediction model (TMPM-ICD9) provides better calibration and discrimination compared with ICISS and injury severity score (ISS). Though TMPM-ICD9 is statistically rigorous, it is not precise enough mathematically and has the tendency to overestimate injury severity. The purpose of this study is to develop a new ICD-10-CM injury model which estimates injury severities for every injury in the ICD-10-CM lexicon by a combination of rigorous statistical probit models and mathematical properties and improves the prediction accuracy. Methods: We developed an injury mortality prediction (IMP-ICDX) using data of 794,098 patients admitted to 738 hospitals in the National Trauma Data Bank from 2015 to 2016. Empiric measures of severity for each of the trauma ICD-10-CM codes were estimated using a weighted median death probability (WMDP) measurement and then used as the basis for IMP-ICDX. ISS (version 2005) and the single worst injury (SWI) model were re-estimated. The performance of each of these models was compared by using the area under the receiver operating characteristic (AUC), the Hosmer-Lemeshow (HL) statistic, and the Akaike information criterion statistic. Results: IMP-ICDX exhibits significantly better discrimination (AUCIMP-ICDX, 0.893, and 95% confidence interval (CI), 0.887 to 0.898; AUCISS, 0.853, and 95% CI, 0.846 to 0.860; and AUCSWI, 0.886, and 95% CI, 0.881 to 0.892) and calibration (HLIMP-ICDX, 68, and 95% CI, 36 to 98; HLISS, 252, and 95% CI, 191 to 310; and HLSWI, 92, and 95% CI, 53 to 128) compared with ISS and SWI. All models were improved after the extension of age, gender, and injury mechanism, but the augmented IMP-ICDX still dominated ISS and SWI by every performance. Conclusions: The IMP-ICDX has a better discrimination and calibration compared to ISS. Therefore, we believe that IMP-ICDX could be a new viable trauma research assessment method.
关键词： Injury severity score (ISS),International Classification of Diseases Tenth Edition (ICD-10-CM),Mortality prediction,Injury mortality prediction for ICD-10-CM (IMP-ICDX)
更新于2025-09-11 14:15:04
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[IEEE 2019 IEEE Milan PowerTech - Milan, Italy (2019.6.23-2019.6.27)] 2019 IEEE Milan PowerTech - Optimal Scheduling of Generators and BESS using Forecasting in Power System with Extremely Large Photovoltaic Generation
摘要： Large scale integration of renewable energy sources (RES) can cause supply demand uncertainty. In Japanese power systems the photovoltaic (PV) generation is growing rapidly. PV forecasting with energy storage systems can be used in Unit Commitment (UC) to reduce these imbalances. In this study Battery Energy Storage systems (BESS) and day-ahead PV forecasting with prediction intervals have been used to examine the imbalances. The day-ahead UC of thermal generators and day-ahead optimal BESS charging and discharging is calculated with different BESS inverter capacities and BESS energy capacities. Then the power shortfall and surplus of PV power in the target day are calculated. The simulation is run for 3 months from April to June 2010 for Kanto area power system of Japan.
关键词： Photovoltaic (PV) forecasting,Unit Commitment (UC),Optimal Power dispatch,Battery Energy Storage Systems (BESS),Prediction Intervals,Mixed Integer Linear Programming (MILP)
更新于2025-09-11 14:15:04
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An adaptive hybrid model for day-ahead photovoltaic output power prediction
摘要： Accurate and stable photovoltaic (PV) output power prediction is important for the secure, stable and economic operation of power gird. However, due to the indirectness, randomness and volatility of solar energy, accurate and stable PV output power prediction has become a very challenging issue. To obtain a more accurate and stable prediction results, an adaptive hybrid model combined with improved variational mode decomposition (IVMD), autoregressive integrated moving average (ARIMA) and improved deep belief network (IDBN) is developed to predict day-ahead PV output power. First, the original PV output power is decomposed into some regular and irregular components by IVMD. Second, the regular components are predicted by ARIMA, while irregular components are predicted by IDBN. Third, the ?nal forecasting results is obtained by summing the prediction results of each component. The prediction performance is validated by comparing with some other models. Experimental results illustrate that the presented model can improve the prediction performance of PV output power than other models.
关键词： PV output power prediction,ARIMA,IDBN,IVMD
更新于2025-09-11 14:15:04
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High-triplet-energy Bipolar Host Materials Based on Phosphine Oxide Derivatives for Efficient Sky-blue Thermally Activated Delayed Fluorescence Organic Light-emitting Diodes with Reduced Roll-off
摘要： Understanding narrative content has become an increasingly popular topic. Nonetheless, research on identifying common types of narrative characters, or personae, is impeded by the lack of automatic and broad-coverage evaluation methods. We argue that computationally modeling actors provides benefits, including novel evaluation mechanisms for personae. Specifically, we propose two actor-modeling tasks, cast prediction and versatility ranking, which can capture complementary aspects of the relation between actors and the characters they portray. For an actor model, we present a technique for embedding actors, movies, character roles, genres, and descriptive keywords as Gaussian distributions and translation vectors, where the Gaussian variance corresponds to actors’ versatility. Empirical results indicate that (1) the technique considerably outperforms TransE (Bordes et al. 2013) and ablation baselines and (2) automatically identified persona topics (Bamman, O’Connor, and Smith 2013) yield statistically significant improvements in both tasks, whereas simplistic persona descriptors including age and gender perform inconsistently, validating prior research.
关键词： cast prediction,Gaussian embeddings,actor modeling,versatility ranking,narrative understanding,persona evaluation
更新于2025-09-11 14:15:04
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The Photovoltaic Output Prediction Based on Variational Mode Decomposition and Maximum Relevance Minimum Redundancy
摘要： Photovoltaic output is affected by solar irradiance, ambient temperature, instantaneous cloud cluster, etc., and the output sequence shows obvious intermittent and random features, which creates great difficulty for photovoltaic output prediction. Aiming at the problem of low predictability of photovoltaic power generation, a combined photovoltaic output prediction method based on variational mode decomposition (VMD), maximum relevance minimum redundancy (mRMR) and deep belief network (DBN) is proposed. The method uses VMD to decompose the photovoltaic output sequence into modal components of different characteristics, and determines the main characteristic factors of each modal component by mRMR, and the DBN model is used to fit the modal components and the corresponding characteristic factors, then the predicted results of each modal component is superimposed to obtain the predicted value of the photovoltaic output. By using the data of a certain photovoltaic power station in Yunnan for comparative experiments, it is found that the model proposed in this paper improves the prediction accuracy of photovoltaic output.
关键词： mRMR,photovoltaic output prediction,feature selection,DBN,VMD
更新于2025-09-11 14:15:04