It is generally recognized that high-temperature treatments, namely annealing, influence the microstructure and the morphology, which, in turn, determine the mechanical properties of polymeric parts. Therefore, annealing can be adopted to control the mechanical performance of the molded parts. This work aims to assess the effect of annealing on the morphology developed in isotactic polypropylene (iPP) injection-molded parts. In particular, a two-step annealing is adopted: the polymer is injected in a mold at a high temperature (413 or 433 K), which is kept for 5 min ; afterward, the mold temperature is cooled down at 403 K and held at that temperature for a time compatible with the crystallization half-time at that temperature . The characterization of morphology is carried out by optical and electronic scanning microscopy. The temperature of the first annealing step does not influence the thickness of the fibrillar skin layer; however, such a layer is thinner than that found in the molded parts obtained without any annealing steps. The second annealing step does not influence the thickness of the fibrillar skin layer. The dimension of spherulites found in the core is strongly influenced by both annealing steps: the spherulite dimensions enlarge by the effect of annealing steps. A model that considers spherulite and fibril evolutions is adopted to describe the effect of molding conditions on the final morphology distribution along the part thickness. The model, which adopts as input the thermo-mechanical histories calculated by commercial software for injection molding simulation, consistently predicts the main effects of the molding conditions on the morphology distributions. Injection molding is one of the preferred processes for obtaining polymeric parts with high geometry accuracy in several fields, including transportation, construction, and biomedicine. Strength and rigidity are two important properties of polymeric parts for applications in the field of structural part production. Polyolefins, even if widely adopted to produce commodities because of their low price, generally do not show sufficient strength and rigidity for such applications. In recent years, the strategies to increase the strength and rigidity of injection-molded polyolefins, especially polypropylene, are attracting relevant research interest . Indeed,Heat treatments, namely annealing, improve polymer properties by removing the residual stress in polymer products, perfecting the crystalline structure, and vanishing the crystal defects that arise during the process ,5. Thus,Few studies are devoted to the analysis of the effect of in-mold annealing on the morphology and crystallization behavior of polyolefins. In these cases, the analysis of the effect of annealing on morphology is even more complicated since several morphological features form during the process: fibrils oriented along with the flow direction close to the part surface and spherulites in the part core . Liao etIn this work, the injection molding process coupled with in-mold annealing was conducted on an isotactic polypropylene (iPP) to assess the influence of in-mold annealing on the morphology developed within the molded parts. A model for the description of the crystallinity evolution was proposed and applied to predict the formation of each morphological feature formed during the process. Particularly, a two-step simulation was conducted: the process evolution was simulated through commercial software for injection molding, then the processing variable evolutions obtained from the first step were used as inputs for the prediction of crystallinity evolution. This approach is expected to empower commercial software for injection molding, giving the possibility to predict the formation of several morphological features (now only isotropic structures can be described), thus, describing the overall morphology of the molded part. Injection molding experiments were conducted on a well-characterized iPP  ,14,15.HAAKE Minijet II by Thermo Scientific  was adopted for producing the specimens with different morphology distributions. This machine is a mini-injection molding system that adopts a pneumatic piston to control the pressure during the molding. The molds for HAAKE Minijet II present a truncated cone shape with a diameter that changes from 50 mm (at the gate side) to 35 mm over a length of about 90 mm. The specimens were obtained with a rectangular cavity having the following dimensions: 1 mm thick, 10 mm wide, and 60 mm long.mold temperature  were cut from the central part of the molded slab in the flow-thickness plane by Leica RM 2265 slit microtome . In particular, a sample was obtained by considering the first 30 mm of the molded part downstream of the injection location and then, as shown in A desktop SEM  was adopted for morphological characterization. Before the analysis, the samples were coated with a thin film of gold by sputtering. Moldflow software  was adopted for the simulations of the injection molding process in the conditions reported in Shear rate and temperature evolutions obtained from the Moldflow simulations were used as inputs for the model proposed to predict the crystallinity evolution in the forms of spherulites and fibrils.The model for describing crystallization evolution is given in a previous paper ,21,22,23x is the flow direction, y is the direction transversal to the flow, Fibrils and spherulites compete for the same amorphous space; their evolutions are given by Kolmogoroff’s Equations The average spherulite radius, The morphology of all the analyzed specimens is composed of a banded-colored area, which corresponds to the fibrillar layer close to the sample surface, and a granular area in the core, corresponding to the spherulitic layer. Sample S shows the largest fibrillar layer covering most of the sample thickness. Previous results  on moldeClose to the sample surface, SEM micrographs acquired in the banded-colored area of the optical micrograph are characterized by tightly packed fibrillar structures. In the transition zone, SEM micrographs show intermediate morphology at the boundary between the fibrillar layer and the spherulitic one. Nuclei, which form at the boundary, cannot grow toward the surface due to fibrils that hinder growth along the direction transversal to the flow. Such nuclei grow only toward the midplane, where the crystallization in the form of spherulites is favored by lower values of shear rate (as discussed below). Therefore, the final morphology in the transition zone is characterized by the presence of not fully developed spherulites with dimensions smaller than those observed for spherulites in the sample core. This finding is consistent with the higher value of nucleation induced by an almost strong flow experienced by the polymer in the transition zone. Close to the sample mid-plane, SEM micrographs acquired in the granular area of the optical micrographs confirm the presence of spherulites. The adopted molding conditions influence the dimension of spherulites. Spherulites significantly enlarge when a second step is carried out. Further, for tests conducted with 433 K mold temperature, similar increases in the spherulite dimensions with respect to the test S were observed. The experimental findings suggest that if the crystallization in the inner part of the specimen occurs during annealing steps, it occurs within high temperatures, at which the nucleation is slower; thus, the crystals are allowed to grow for a higher extent before impingement with respect to case S.Numerical simulations with Moldflow for the cases reported in The temperature is high at the first contact of the melt with the cavity surface; after that, it decreases down to the temperature of the mold. In case S, the temperature decreases fast toward 298 K; in case 413_FA, the temperature decreases toward 413 K since the whole mold is kept at this temperature for 5 min. At the end of annealing, the temperature decreases toward 298 K. Simulations consistently reproduce the main features of the experimental temperature evolutions. Particularly, the temperature decrease from the melt temperature down to the cavity surface temperature (temperature decrease rate depends on the adopted conditions) is consistently reproduced by the simulation.Secondly, morphology distributions within the samples were predicted with the model for describing crystallization and compared with optical micrographs. To this aim, Moldflow temperature and shear rate evolutions were adopted as input for the crystallization model. As already described, the crystallization model couples the model for the description of the molecular stretch to the model for the description of crystallinity evolution, allowing morphology distribution prediction.The morphology is determined by the simultaneous local evolutions of temperature, flow rate, and relaxation time, see Equation . Close to the cavity surface a,c, at t−1) and become zero at about 9 s. The slope change in the shear rate evolutions at 5.5 s corresponds to the pressure release at the end of the holding stage. At that time, the temperature remains constant for the vanishing of the hot convective term. The shear rate follows the same trend at both positions considered along the thickness; obviously, the values close to the cavity surface are significantly larger than the values calculated toward the midplane. Concerning temperature, it decreases within a higher temperature range with respect to case S due to the high mold temperature adopted. Temperature and shear rate evolutions follow the same trend already discussed for case 413_FA. However, the temperature remains within higher values for longer times. Fibers form only at the sample surface, namely at a normalized thickness of 0.5 a,c. For In condition S, crystallization occurs at low temperatures, within a temperature range of 360–315 K, due to the high cooling rate experienced by the polymer during the process. When a single annealing step is adopted (cases 413_FA and 433_FA), the two crystallinity evolutions overlap. The crystallization occurs during the cooling of the whole mold toward the ambient temperature. The cooling rate experienced by the polymer is lower, and crystallization occurs at a higher temperature with respect to case S, within a narrower range, 380–360 K. In both cases, crystallization occurs during the annealing step at 403 K when the temperature is constant along the whole cavity thickness and under quiescent conditions. Moreover, Moldflow simulations consistently predict crystallinity evolutions. In case 413_SA, the lower temperatures experienced by the polymer during the first annealing stage induced faster nucleation and growth of the spherulitic structures with respect to case 433_SA; thus, crystallization occurs earlier than in case 433_SA.On the basis of the considerations reported above on the crystallinity evolution and its dependence on the molecular stretch, the predicted morphology distribution along the cavity thickness for all the cases examined in this work was compared with experimental observations. An increase in the fibrillar kinetics at high temperatures would favor the fibrillar layer formation when a 433 K mold temperature is adopted. Such an effect were also observed in the experimental investigation conducted on the iPP fibrillar kinetics . MoreovePredictions of radii are in the same order of magnitude as the experimentally measured radii. Samples 413_FA and 433_FA show the same average radii, which are larger than the S case. The second annealing step at 403 K leads to a significant increase in the spherulite dimensions: cases 413_SA and 433_SA show similar spherulite dimensions. The predictions almost correctly describe the dependencies upon the molding conditions of the spherulite’s dimensions. However, the dimensions of spherulites are underestimated in the standard case and in the cases with a single annealing step. The spherulite’s crystalline functions were defined by extrapolating the results of the experimental investigation conducted in conditions far from the ones experienced by the polymer during the process. For the case with a second annealing step at 403 K, crystallization occurs under isothermal and quiescent conditions similar to those adopted during the experimental investigation on crystallization phenomenon , whereasThis work analyzes the effect of processing conditions, concerning mold temperature and in-mold annealing, on the morphology developed within the molded parts. Molded parts were characterized by optical and electronic microscopy: they show a fibrillar layer close to the cavity surface and a spherulitic core. It was found that morphology was strongly affected by processing conditions: fibrillar layer reduced by applying in-mold annealing, due to the increase of the mold temperature; the second annealing step had a negligible influence on the fibrillar layer extension. Furthermore, the thickness of the fibrillar layer is scarcely influenced by the mold temperature adopted during the in-mold annealing. Spherulites’ dimensions enlarged due to the effect of both the mold temperature and annealing steps. To predict the morphology distributions induced by the process, a model that couples the description of the fibrils and spherulite evolution with the description of the molecular stretch evolution was adopted. The injection-molding tests in standard conditions and in presence of in-mold annealing were simulated by Moldflow. Predicted temperature and shear rate evolutions were used as input to the proposed model for predicting morphology distributions. The model was able to describe the formation of fibrils close to the sample surface and spherulites in the core of the sample. The main effects of the molding conditions on the morphology distributions were effectively predicted by the model. In the presence of in-mold annealing, a decrease in the fibrillar layer thickness with respect to the standard case was predicted. Further, the predicted fibrillar layer formed during the early stage of the process; thus, it is not influenced by the second annealing step. However, the effect of the mold temperature in the range of considered temperatures was overestimated by the predictions. The model almost accurately described the increase in the spherulites’ dimensions induced by the in-mold annealing. In particular, the second annealing step had a significant effect on the spherulites’ dimensions. The experimentally observed and predicted spherulites’ dimensions were of the same order of magnitude for all considered cases. It is worth pointing out that, with the proposed approach, an acceptable description of the final morphologies (fibrils and spherulites) distribution in molded parts can also be obtained by adopting simulations conducted with commercial software that does not consider the fibril content at all. However, a more accurate prediction of the morphology distributions could be achieved including the models of the different morphology evolution in simulation software and considering the effects of crystalline structures on the polymer’s properties .


Graph neural networks (GNNs) have been widely used in various graph analysis tasks. As the graph characteristics vary significantly in real-world systems, given a specific scenario, the architecture parameters need to be tuned carefully to identify a suitable GNN. Neural architecture search (NAS) has shown its potential in discovering the effective architectures for the learning tasks in image and language modeling. However, the existing NAS algorithms cannot be applied efficiently to GNN search problem because of two facts. First, the large-step exploration in the traditional controller fails to learn the sensitive performance variations with slight architecture modifications in GNNs. Second, the search space is composed of heterogeneous GNNs, which prevents the direct adoption of parameter sharing among them to accelerate the search progress. To tackle the challenges, we propose an automated graph neural networks (AGNN) framework, which aims to find the optimal GNN architecture efficiently. Specifically, a reinforced conservative controller is designed to explore the architecture space with small steps. To accelerate the validation, a novel constrained parameter sharing strategy is presented to regularize the weight transferring among GNNs. It avoids training from scratch and saves the computation time. Experimental results on the benchmark datasets demonstrate that the architecture identified by AGNN achieves the best performance and search efficiency, comparing with existing human-invented models and the traditional search methods. The learned node representations could be employed to deal with different tasks efficiently.Graph neural networks (GNNs) Micheli,  have emeThe success of GNNs is usually accompanied with careful architecture parameter tuning, aiming to adapt GNNs to the different types of graph data. For example, attention heads in the graph attention networks  has attracted increasing research interests   is not suitable to GNN architectures. The parameter sharing trains common weights and transfers them to every newly sampled architecture, aiming to avoid training from scratch and measure the new architecture quickly. But it fails to share weights between any two heterogeneous GNN architectures, which have the distinct output statistics. The output statistics of a model is defined by the mean, variance, or value interval of its neuron activation values. Suppose that we have weights deeply trained in a GNN architecture with Sigmoid activation function, bounding the neural outputs within interval . If we transfer the weights to another architecture possessing Linear function with loose activation interval , the neural output values may be too large to be back propagated steadily by the gradient decent optimizer.However, the direct application of existing NAS algorithms to find GNN architectures is non-trivial, due to the two challenges as follows. We propose the automated graph neural networks (AGNN) to tackle the aforementioned challenges. Specifically, it could be separated as answering two research questions. (i) How do we design the search controller tailored to explore the well-performing GNN architectures efficiently? (ii) Given the emerging heterogeneous GNN architectures during the search progress, how do we make the parameter sharing feasible? In summary, our contributions are described as follows:elementary, deep and scalable GNN architectures. The search space incorporates the recent techniques, such as skip connections and batch training, to explore the promising models on large-scale graphs.We build up the most comprehensive search space to cover the efficient controller by considering the key property of GNN architectures into search progress—the variation of node distinguishing power with slight architecture modifications.We design an effectiveness of transferred weights in the new architecture.We define the heterogeneous GNN architectures in the context of parameter sharing. A constrained parameter sharing strategy is proposed to enhance the functional We conduct the extensive experiments to search the elementary, deep, and scalable GNNs, which delivers the most superior results on both small and large-scale graph datasets. Comparing with existing NAS, AGNN achieves the double wins in the search efficiency and effectiveness.The core idea of GNNs is to learn the node embedding representations recursively from the representations at the previous layer. The graph convolutions at each layer is realized by a series of manipulations, including the message passing and self updating. A variety of GNNs based on spatial graph convolutions has been developed, including GNN models with the different aggregation mechanisms  (∈ℝdk)(×dk−1) hidden units specifying dk) attention function used to compute III) number of attention heads; (IV) aggregation function; (V) combination function; and (VI) activation function.Deep dimensions. We include dimension of (VII) skip connections to allow the stacking of deep GNNs. To be specific, at layer k, the embeddings of up to k − 1 previous layers could be sampled and combined to the current layer's output.Scalable dimensions. The dimension of (VIII) batch size is included to facilitate the computation on large-scale graphs.We highlight that most of existing search space only cover the elementary dimensions -based NAS, a recurrent neural networks (RNN) encoder is applied to specify the neural architecture strings , as shown in The conservative exploiter is applied to keep the best architecture found so far. In this way, the following architecture modifications are performed upon a reliable parent architecture, which ensures fast exploitation toward the better offspring architectures in the huge search space. If the offspring GNN outperforms its parent, the best neural architecture is updated; otherwise, it will be kept and reused to generate the next offspring GNN.via choosing the dimensions deserved for exploration. As shown in The guided architecture explorer is proposed to modify the best architecture, c, an RNN encoder is implemented to sample a series of new modules. These modules are potential to be used to update the n layers in the preserved GNN correspondingly. First, a subarchitecture string is generated by removing the original modules of the concerned dimension. This subarchitecture represents the input status that asks for module padding. Second, following an embedding layer, the n new modules are sampled layer by layer.As shown in the middle part of c decides the sampling probability distribution at layer k as: c denotes the trainable parameters, and m denotes the module cardinality. The module at layer k is randomly sampled based on distribution Specifically, RNN encoder of dimension c is: Ec is, the higher is the probability of exploring uncertain dimension c.It is responsible to choose the architecture dimensions to modify the preserved GNNs. We note that NAS is encouraged to explore the search space along the direction with a great amount of uncertainty. The uncertainty of a dimension could be defined by the entropy of sampling probability. Formally, the decision entropy of dimension Ec, ⋯ } of all the dimensions, the modification guider randomly chooses dimensions s = 1 to achieve the goal of minimum architecture modification. We provide the hyperparameter study of s in Appendix, which shows the model performance generally decreases with the increasing of size s. This validate our motivation to explore the search space of GNN architectures with small steps.Given the decision entropies {⋯ , We modify the modules of the preserved best architecture according to list c by the following rule  by evaluating the new offspring architecture. Bc denotes the reward baseline of dimension c for variance reduction in reinforcement learning. Let Mb and Mo denote the model performances of the preserved best architecture and the new offspring, respectively. We propose the following reward shaping: R = Mo − Mb, which represents the model performance variation due to the architecture modification. Using the same reward, RNN encoders of all the s dimensions within list s. The conventional controller used in GraphNAS  to adapt these connection weights to the new successive layers.Q1: How does the elementary GNN architecture discovered by AGNN compare with the human-invented models and the ones searched by other methods? Q2: How effective is AGNN to build up the deep architecture? Q3: How scalable is AGNN to explore superior model on large-scale graphs? Q4: How efficient is the proposed RCNAS controller compared with the ones in other search methods? Q5: Whether the constrained parameter sharing transfers weights effectively to the offspring architectures? We provide the explanation of discovered architectures in We experiment on the node classification task with the goal of answering the five research questions. To study the neural architecture search of elementary and deep GNNs, we use the benchmark node classification datasets of Cora, Citeseer, and Pubmed  are considered for fair comparison, except the one combined with the pooling layer or other advanced techniques. Considering the elementary GNNs, we apply baseline models of Chebyshev .Author QS was employed by LinkedIn. Author RC was employed by Samsung Research America. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.The views, opinions, and/or findings contained in this paper are those of the authors and should not be interpreted as representing any funding agencies.


Furthermore, we computed several EDA indices from the EDA signals obtained from different sites and evaluated their efficiency to classify cognitive stress from the baseline state. We found a high within-subject correlation between the EDA signals obtained from the finger and the feet. Consistently high correlation was also found between the finger and the foot EDA in both the phasic and tonic components. Statistically significant differences were obtained between the baseline and cognitive stress stage only for the EDA indices computed from the finger and the foot EDA. Moreover, the receiver operating characteristic curve for cognitive stress detection showed a higher area-under-the-curve for the EDA indices computed from the finger and foot EDA. We also evaluated the robustness of the different body sites against motion artifacts and found that the foot EDA location was the best alternative to other sites.The most traditional sites for electrodermal activity (EDA) data collection, palmar locations such as fingers or palms, are not usually recommended for ambulatory monitoring given that subjects have to use their hands regularly during their daily activities, and therefore, alternative sites are often sought for EDA data collection. In this study, we collected EDA signals ( In recent years, there has been a tremendous increase in the popularity of wearable devices, which has greatly increased the feasibility of non-invasive and continuous physiological data collection ,2. ElectThe most widely used and the most responsive sites for EDA signal measurement are the finger pads and palmar surfaces ,18,19. H2) and are often considered the best sites for EDA recordings [2), which has also been explored by researchers for EDA data collection [Skin conductance at a particular body site depends on several factors, such as the density of sweat glands and their relative sizes, the sweat output of the individual glands, and also the nerves that control the sweat glands of the site ,23. Palmcordings ,25,26. Allection ,26.As an alternate measurement site, the wrists were suggested by many researchers in the past few years ,27,28. HAnother important factor to be considered about the EDA for finding alternate sites is the hydration time . DependiWhile previous studies ,32 have In this study, we considered four different body sites  that have high sweat gland density, and these sites were also previously reported to have highly correlated EDA signals when compared to the finger EDA ,30,32,35We collected four channels of EDA signals from different body locations, namely the forehead, neck, fingers, and foot. Similar to most studies ,30,32,35The experimental protocols for this study are shown in The EDA data collected were resampled at 8 Hz from 100 Hz. We then used a Butterworth lowpass filter of order six and cutoff frequency of 0.6 Hz (which is well above the frequency range of EDA dynamics) . Since t<0.4 Hz) ,41, the <0.4 Hz) , which i<0.4 Hz) .We then used the comments noted during the experiment to segment the data into different stages, where each segment corresponds to a particular phase of the experiment . We computed traditional EDA indices, such as the number of skin conductance responses, mean and variance of skin conductance level, and the mean and variance of phasic signals, from each segment of the four EDA locations . Since tMany of the previous publications reported Pearson’s correlation to compare the EDA signals from different body sites. However, as mentioned in , correlaThe phasic signal, which is believed to contain most of the information, does not always match very well to the corresponding finger phasic signal . Therefore, for a more comprehensive comparison between the measurement sites, we computed correlations for the phasic and tonic components among the signals from different measurement sites.While within-subject correlation has been reported in most of the previous studies, none of them compared EDA-derived indices that are typically used for the identification of sympathetic stimuli. We computed EDA indices from each body location’s signals and compared their efficacy at differentiating between the baseline stage and the cognitive stress stage (SCWT). To check the statistical difference of the EDA indices between the baseline and the cognitive stress stages, we performed a pairwise t-test if the data of two groups were normally distributed. Otherwise, we used the two-sided Wilcoxon rank sum test . We usedOne of the significant motivations behind exploring alternative sites for EDA data collection is to find a comfortable body site for electrode placement that is also robust against motion artifacts. Since a major focus is on ambulatory EDA monitoring, it is essential to analyze the effect of daily movements on the EDA collected from different measurement sites ,46. In tTo compare the data quality among different body locations, we first computed the power spectrum of the EDA signal using the Welch periodogram with 50% data overlap, and then defined a noise band (frequency > 0.4 Hz). The reason we choose 0.4 Hz is that most of the ANS activity (sympathetic and parasympathetic) is captured within the frequency range of 0.04 to 0.4 Hz ,40. TherThe higher the We computed the Pearson correlation between the finger EDA and EDAs from other body sites during the “no movement” phases . Since during the first two phases of the protocol there is no significant motion artifact, for accurate correlation we used continuous 4 min data (baseline and SCWT) for calculating the Pearson correlation. We also observed a larger between-subjects variability , which suggests that even though for some subjects the forehead and neck EDA were highly correlated with the finger EDA, for others this was not the case. As mentioned earlier, the correlation using only the raw data could be misleading because of the underlying trends in the data; hence, we also computed correlation for the phasic and the tonic components of the EDA. As shown in We evaluated the efficacy of the EDA calculated from each measurement site to determine its responsiveness to cognitive stress. In this experiment, cognitive stress was induced by the SCWT, which can be considered a mild stress. We first performed the one-sample Kolmogorov–Smirnov test  on the dIt can be seen from We also evaluated EDA metrics for classifying differences between the baseline and the SCWT stages. We computed the receiver operating characteristic (ROC) curves for EDA metrics to classify between the baseline and the cognitive test stages. We first computed the true positive rate and the false positive rate for each feature at a variety of thresholds. The ROC curve was then obtained by plotting the true positive rate against the false positive rate. In this case, the positive class refers to the cognitive stress stage and the negative class represents the baseline stage. In order to evaluate the effect of motion artifacts on EDA signals from different measurement sites, we computed the power spectrum of the EDA signals during motion artifact and the fraction of total power in the noise band (frequency > 0.4), During weightlifting, the neck EDA was affected the most by motion artifacts, which could be because of the periodic contraction and expansion of the muscles in the neck. Since the subjects used their hands for lifting weights, the finger EDAs were distorted on many occasions, as expected. As can be seen from In this study, we collected EDA signals from four measurement sites  that are known to have high eccrine gland density. This study is one of the first studies to compare popular EDA indices obtained from different measurement sites. We also compared the robustness of different body sites against measurement noise.Unlike many studies that only computed correlations between raw EDA signals, we computed within-subject correlations for the raw EDA and tonic and phasic components, which eliminated the risk of false correlations due to the trends in the signal. The within-subject correlation was the highest between the finger and the foot. The forehead and neck EDA showed moderate correlation with the finger EDA. However, for the phasic component, the correlation was rather low. This could be due to a lack of sufficient hydration time, which is required for the sweat glands to be innervated.The effect of hydration time on the forehead EDA can be seen when we examine the number of subjects that showed a correlation higher than 0.5 between the finger and the forehead or the neck in different stages starting from the baseline. We computed several key EDA indices from different body sites and compared their efficacy to differentiate between the baseline and cognitive stress  stages. We observed that none of the EDA indices showed significant differences between the baseline and cognitive stress for the forehead and neck EDA. On the other hand, EDA indices computed from the finger and foot showed significant differences between the baseline and SCWT stages. The ROC curve computed using the EDA indices from the finger and the foot had a higher area-under-the-curve. SCWT induces mild cognitive stress and for some subjects, they may not experience any stress, which explains why the EDA from the finger and the foot showed moderate accuracy to differentiate stress from the baseline condition.The effect of motion artifacts on the EDA signals obtained from different measurement sites shows that the neck is the most sensitive to motion artifacts, while the forehead is the most robust. However, the forehead needs a prolonged amount of hydration time for producing meaningful EDA responses, which could be an issue for short-term applications. Moreover, we observed a high variability in correlation between the forehead and the finger EDA among subjects, which means that for some subjects, the forehead EDA did not produce similar responses to the finger EDA. In cases where finger or palmar sites are not available, we recommend the foot location, because good signals can be obtained from it. The use of the feet may be preferred in the case where hands are required for other daily tasks. For example, divers use their hands for many diving-related tasks, so placing EDA sensors on the feet may be more preferable. We have previously shown that the EDA signal may be used to predict seizures due to oxygen toxicity ,48. HencThe first limitation of the study is the lack of sufficient female subjects, which may limit the generalizability of this study. Second, the motion artifacts we considered were limited to a mild level. There could be specific cases where one of the body sites examined might be affected more or less depending on the situation . We considered walking and weightlifting because these two cases are the most common daily activities. Human subjects typically walk every day and use their hands to perform daily tasks. Moreover, since fingers and feet are two of the best sites to collect EDA signals, we wanted to explore how motion artifacts affected those two locations. For signals with higher motion artifacts, our recently developed convolutional autoencoder-based model may be applied to recover motion artifact-free clean EDA signals . AnotherThis study collected wearable EDA signals from four different measurement sites and compared them in terms of the within-subject correlations with the finger EDA, which is considered the reference. To the best of our knowledge, this is the first study to examine EDA indices from different measurement sites and compare their efficacy to differentiate induced cognitive stress in subjects. Finally, the study compared the robustness of EDAs obtained at different sites against motion artifacts. Based on the results, we recommend the foot be used as an alternative site for EDA data collection, especially when palmar sites or fingers are not available or suitable for placing EDA electrodes. The forehead is a potential alternative site for EDA data collection because of its comparative robustness against motion artifacts, however its low sensitivity to stimuli, prolonged duration of hydration time, and high variability among subjects all suggest that it is not an optimal site; thus, the foot is preferred.


Today, many maritime nations have been promoting boat sports proactively, including sailing races. As sailing races are large-scale regattas that require massive workforces to monitor the game fairly; however, with limited match budgets and labors, Internet of Things (IoT) technology supports monitoring games has become a trend. This article proposes a robot umpire system in sailing races based on Artificial Intelligence (AI) techniques, using drones and AIoT technology to monitor sailing matches. When a large number of sailboats are in a match, and each sail along different routes, drones can monitor the entire game simultaneously. The features of this proposed approach are (1) The system recognizes images by Faster R-CNN, judging whether a sailboat uses a motor to accelerate; (2) The system detects conditions by edge computing; when cheating behaviors happen, it can notify the event holder immediately; (3) Advanced drone route plans can avoid collision incidents; (4) Improve the system recognition by federated learning. This study has implemented an experiment with real drones and installed IoT equipment on the drones for taking videos and recognizing. The experimental result has shown that the proposed approach is feasible and benefits the match's fairness. Additionally, umpires can review the violation details from the videos taken by the drones, supporting evidence for judging. Sailing is an Olympic sport; it was an official Olympic sport since the first modern Olympic in 1896; yet, due to adverse weather conditions, sailing was canceled in the first event. On the other hand, sailing is a large-scale sport; it requires several umpires taking boats to monitor and maintain fair judgments; consequently, sailing matches need higher labor costs. Despite having many umpires to monitor the game, the time-consuming sport sometimes causes human errors; hence, the addition of robot umpires can enhance the game's fairness. Sailing takes place on the vast sea, the starting point is usually a virtual line, and the competition venue is often a specific area that varies according to the weather conditions. Reaching the first mark usually needs to sail upwind with a zigzag trajectory, which requires massive concentration and consumes heavy physical strength. Sailors control the sailboat moving forward by wind blowing; it is a skill-based and physical strength-assisted sport. A rudder is usually a movable appendage mounted under the boat; moving it can control the direction of the sailboat, which also changes the direction of the sailboat and the wind, as well as the angle between the sail surface and the wind.Currently, most sailing matches require umpires to take on a boat to monitor and judge the game. With the scale of the game, massive workforces are needed to proceed with the contest; nonetheless, the limited numbers of umpire boats, umpires, and moving ranges, combined with the differences in boat speeds and wind changes, these situations make sailboats scatter in various corners around the sea. Cheating behaviors may occur when umpire boats are not around or when a boat gets stuck in calm waters away from any umpire boats. A robot umpire system is necessary to cope with such a situation and widely monitor the game. A robot umpire system requires intelligence with high mobility; the system can notify the event holder when cheating happens. The equipment in Wang et al.  analyzesThis research presents an AI robot umpire in sailing races by drones and the AIoT technique to monitor the game. Due to the vast ranges of the game, drones can resolve the territory issue. The proposed drone is designed with an IoT development board and a camera; the AI module in the IoT development board will detect the videos and send the results to the umpires for review. The features of this article are: (1) The equipment uses an IoT development board and a camera to monitor sailboats; (2) The system recognizes images by Faster R-CNN, judging whether a sailboat uses a motor to accelerate; (3) The system detects conditions by edge computing; it can notify the event holder immediately when cheating happens; (4) Advanced drone route plans can avoid collision incidents, and the route plan enables drones to work together and take wide-range videos; (5) Improve the system recognition by federated learning. The proposed system helps umpires to control competition rapidly. Furthermore, the method enables umpires to monitor the entire sailing game from multiple angles. With the Faster R-CNN technique, the system can detect various objects, and the federated learning can update the AI module in the IoT development board and improve the AI recognition. The experiment in a real sailing competition has proved the feasibility of the proposed approach. The main contributions of this article are as follows: (1) Use drones to monitor the competition; (2) Use video to see if there are any violations, and use video evidence to make judgments; (3) Use drones to monitor electronic referees, which will help reduce event personnel costs, (4) Federated Learning improves the recognition of the system.The suggested AI robot umpire employs drones with cameras to monitor sailing matches and detect violations. Li and Ye  presentsAs mentioned in Jiang et al. , multi-oIn Ghimire and Rawat , it is mXu et al.  has poinK+1 types, and the other outputs the reset of the bounding box regression. The formula of the loss function is:This article utilizes Faster R-CNN to conduct object recognition in pictures, cutting the recognized boat objects into pieces to check if any of them run a motor to accelerate. The flow chart of Faster R-CNN is illustrated in tu represents the prediction result, u means types, and v is the real result.Function (1) is the loss function for regression, V is the ground truth, which adopts smoothL1 loss toward the regression of the checking box. The calculation is as follows:The second part is the loss regression, where Regarding image sorting, most of the processing time is consumed in the convolution layer, and the system will spend less time in the fully connected layer. However, target detection algorithms usually need to handle massive ROI, which spends more time on the fully connected layer. Therefore, Truncated SVD can easily accelerate on the large fully connected layer; the system can rapidly highlight the target objects in images and further detect if the object uses a motor.PKi), Private Key (PRi), and a certificate (Ceri). Each device will use the public key of the server to encrypt the parameters of the device and uses the Hash function to calculate the message so that the server can confirm the integrity of the message, and the server will optimize the parameter calculation after collecting the information of each device. Federated learning can sort information based on the types of information, primarily are (1) Horizontal learning; (2) Vertical learning; (3) Federated transfer learning. Our study employs horizontal learning to train the parameters, and the process is as follows:Federated Learning mainly uses the Public Key Infrastructure architecture for private communication, in which the Hash function is used for message integrity confirmation. First, each device has a Public Key , and the location of vertexes can be identified in a GPS. Thus, shape the undirected graph G = , where E is the side set; set there are m numbers of drones, each of them will be at a different vertex. The vertex coloring theory will separate the drones on different vertex, m numbers of drones will have K numbers of colors on the map as adjacent vertex should mark with different colors. Therefore, the drones will cruise from different vertexes to achieve comprehensive monitoring. In other words, because each drone locates differently, the situation of repeat monitoring will not happen. This article utilizes a graph coloring problem to plan routes for drones. Because sailing moves in a large range, it requires several drones to monitor the match. We set n numbers of vertexes (The article develops an AI robot-umpire system in sailing, using drones to monitor the matches, and the IoT development board of the system will deliver the detection results to the server when noticing violations. The hardware and software equipment is listed in With the popularity of sea sports, the diversity has also increased, especially in boat competitions. However, the large-scale venue also makes it challenging for human monitoring; for example, the limited number of umpires cannot notice boat violations in detail at all times. This research constructs an AI robot-umpire system in sailing races; the drones can conduct comprehensive monitoring and enhance the game's fairness. Using the Faster R-CNN technique, the system fairly monitors the game; the experimental result has proved the feasibility of checking violations and sending judgments to the umpires for the final ruling. In the future, this suggested approach can further monitor moving trajectories, detecting which boat reaches the mark first and sending the result back to the umpires. Furthermore, drones can monitor the entire match and obtain the game conditions from diverse angles, the development will significantly improve the fairness of the game.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.CW: study conception and design, analysis and results, draft the manuscript preparation, reviewed the results, and approved the final version of the manuscript.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Problems such as redundancy of detection model parameters make it difficult to apply to factory embedded device applications. This paper focuses on the analysis of different existing deep learning model compression algorithms and proposes a model pruning algorithm based on geometric median filtering for structured pruning and compression of defect segmentation detection networks on the basis of structured pruning. Through experimental comparisons and optimizations, the proposed optimization algorithm can greatly reduce the network parameters and computational effort to achieve effective pruning of the defect detection algorithm for steel plate surfaces. Applying defect detection segmentation algorithms to real industrial production scenarios, hardware resources are a challenge that must be faced . ComplexModel pruning is a fast and effective way to compress neural networks by cutting out unimportant neurons or filters to obtain a small network model with small storage capacity and fast inference. Model pruning can inherit the weights of the network before pruning, so the model can be pruned to achieve better optimization results.Model pruning is a fast and effective way to compress neural networks by cutting out unimportant neurons or filters to obtain a network model with small storage capacity and fast inference. Model pruning inherits the weights of the network before pruning, so model pruning allows for better mobile deployment and better optimization.For real-time applications such as surface EMG signal processing , gesture1) A model pruning method based on improved geometric median filter pruning is proposed on the basis of structured pruning.2) The pruning method and pruning process are improved by performing model acceleration and fine-tuning in the structured pruning process, and determining whether the pruning end condition is satisfied by the evaluation function to improve the pruning compression efficiency.3) After experimental comparison, the improved geometric median filter model-based pruning method proposed in this paper outperforms other classical pruning methods. And the pruning algorithm has better detection performance and pruning efficiency in steel plate surface defect segmentation detection.The key contributions of this work are:The rest of this paper is organized as follows: In recent years, in order to perform more complex information processing tasks, deep learning-based neural network models have become deeper and deeper, also making them increasingly computationally intensive, making it difficult to deploy neural networks on devices with scarce computational resources or with strict latency requirements . As a reDenseNet  is a denThe above-mentioned deep learning-based model compression methods still have problems such as requiring large hardware resources for acceleration, high redundancy, the stability and robustness of the network after model compression is difficult to be guaranteed in complex environments, and the network model has insufficient self-adaptability.There are two main types of model pruning methods: unstructured pruning and structured pruning. Unstructured pruning prunes the neuron or connection weights, which means that some non-0 elements in the network calculation are set to 0, or the dense connections of the network are turned into sparse connections, turning the original dense matrix operation into a sparse matrix operation, as shown in Structured pruning is a type of pruning at the filter level, which focuses on pruning the filters with smaller contributions in each layer of the network. When the filter van value (the filter's impact factor) is less than a set range, the network is structured to prune redundant filters according to the van value, as shown in Since the convolution kernel obtained after pruning is sparse, and most GPUs today do not provide additional acceleration for sparse matrix operations, this results in a pruned network that is not accelerated in any way compared to the original network, but may be slower.Therefore, structured pruning is now a more general approach, and is relatively more efficient than unstructured pruning methods. For the use of the pruned network does not require the support of specific hardware platforms, computational libraries, effectively avoiding the drawbacks of unstructured pruning and enabling direct deployment on the mainstream deep learning frameworks nowadays .Model-structured pruning requires a criterion to select the filter to be pruned, i.e. the filter’s magnitude value. The most common pruning criterion is that the filter's parametric value is compared to some threshold value and if it is below the threshold, the filter is set to zero, i.e., the filter is pruned and pruned.Unlike the previous methods, geometric median filter-based pruning compresses the convolutional neural network model by removing redundant filters. Geometric median filtering works by calculating the geometric median of the filters within the same layer and, depending on the properties of the geometric median, filters near the geometric median can be represented by the remaining filters. Therefore, pruning the geometric median filter does not have a substantial negative impact on the model performance.In d-dimensional space, given any set of n points: In which, = {1, …, n}; def means that the The geometric median is a classical robust estimator of data centeredness in Euclidean space and is used when pruning the model to obtain common information about all filters within a single layer i as the geometric median for that layer In which, The core idea of geometric median filtering is that if there are filters within layer i that are close to geometric median In The geometric median is a classical robust estimator of data-centricity in Euclidean space. This shows that the information of the selected filter The pruning flow chart based on geometric median structured pruning is shown in The structured pruning process in this paper includes updating the mask, updating the indirect sparsity and updating the module. After the pruning process, the model is accelerated and refined to optimize the model. Finally, an evaluation score is calculated to determine whether the end condition is met. If the end condition is met, the pruned and compressed model is output; if not, the pruning process continues.Geometric median filtering algorithms can effectively improve the compression rate of neural networks and reduce detection model redundancy. The pruned detection model can be deployed to portable devices for faster processing .In this paper, a model pruning algorithm based on geometric median filtering is used to compress the steel plate surface defect detection network and implement a model pruning defect segmentation detection algorithm based on geometric median filtering to reduce the number of parameters and computational effort of the detection model.The Severstal dataset was released open source on the competition platform Kaggle. The Severstal dataset contains 12,568 images from the training set and 1,801 images from the test set. There are 5,902 defect-free images and 6,666 defective images in the training set. The number of defective and non-defective images in the dataset is roughly equal, and most of the images have no defects or contain only one type of defect .All images in the Severstal dataset have a vertical and horizontal resolution of 256 and 1,600 respectively. There are four types of steel surface defects in the Severstal dataset, as shown in The Severstal dataset contains a large variation in morphology between different defects on the surface of steel plates, both large defects such as scratches and scrapes, and very small defects such as pits and edge cracks.The extremely large span of defect scales places high demands on the defect detection segmentation algorithm: it has to focus on the details to achieve fine segmentation; and it has to focus on the global picture and have sufficient sensory field for large scale defects. These factors make feature extraction and detection segmentation of the network difficult and lead to the need for pruning and compression of the defect detection model.The algorithm research and network training in this paper were conducted on a laboratory server. The specific computer systems used and the configuration of the experimental environment are shown in This paper uses relevant open source libraries and toolkits to implement the overall algorithmic procedure based on the good ecology and scalability of the Python language and the open source framework PyTorch .These open source tools greatly save the development time of the defect detection and segmentation procedure in this paper, thus allowing more time and effort to be devoted to the research, improvement and experimentation of the structured pruning algorithm.In order to verify the practical effectiveness of the proposed defect segmentation algorithm based on geometric median filter pruning, experiments with different pruning rates were conducted on different models under the same conditions to test the effect of different pruning rates on the accuracy of the models.Since the main network layer of the pruned model is the convolutional layer, this paper only detects pruning on the convolutional layer of the detection model, and does not perform pruning experiments on the fully connected layers, Batchnorm layers, etc.The input size of the model only affects the computational volume of the model and does not affect the number of model parameters. Therefore, the input size was set to  for the model pruning experiments, i.e., the simulated input image size was 64 × 64 for the 3-channel image.The ResNet50 model has good performance in image recognition and localization tasks . The ResThe FPN-ResNeSt50 model is an improved fusion of the FPN (Feature pyramid networks) and the ResNeSt50 model , with poIn this paper, ResNet50, ResNeXt50 and FPN-ResNeSt50 are used as the detection models for defects on the surface of steel plates, and pruning experiments and validation are performed on them.The effect of different pruning rates on the ResNet50 model is shown in The effect of different pruning rates on the ResNeXt50 (32 × 4d) model is shown in However, the structured pruning effect of the model was not evident at smaller pruning rates in the early stages due to the ResNeXt50 (32 × 4d) model having a 32-component group convolution, resulting in a smaller pruning rate.The effects of different pruning rates on the FPN-ResNeSt50 model are shown in Comparing The model pruning algorithm based on geometric median filtering prunes and compresses the steel plate surface defect segmentation model based on depth feature fusion, and experiments with different pruning rates were conducted on it under the same conditions to test the effect of different pruning rates on the accuracy of the FPN-ResNeSt50 model, and the detection results are shown in At a pruning rate of 40%, the defect detection accuracy starts to gradually decline, so at a pruning rate greater than 40%, it will lead to the loss of important parameters of the model, resulting in a serious decline in accuracy. In contrast, at a pruning rate of 10%–30%, the model accuracy is able to maintain a low loss of accuracy.The test results show that when the pruning rate is small, pruning brings regularization to the network and enhances the generalization performance of the network; when the pruning rate is large, the characterization ability of the network is severely damaged and the performance of the model decreases significantly.In order to solve the problems of large number of model parameters and difficulty in applying the model to actual plant equipment, this paper investigates the defect segmentation detection algorithm based on geometric median filter pruning. Based on the structured pruning, a model pruning algorithm based on geometric median filtering is proposed to prune and compress the defect segmentation detection network, which greatly reduces the network parameters and computational effort and improves the generalization ability of the model. Through experimental comparisons and optimizations, the detection accuracy of steel surface defects is improved. Meanwhile, the parameters and computation of the detection model are reduced. The pruning and compression algorithm proposed in this paper has good prospects for application in the segmentation and detection of defects on steel plate surfaces. Good pruning algorithms can be applied to a variety of factory embedded or portable mobile devices and can meet the demand for real-time scene detection. In the future, there is still a long way to go in model pruning and compression research.


Recently, remote sensing satellites have become increasingly important in the Earth observation field as their temporal, spatial, and spectral resolutions have improved. Subsequently, the quantitative evaluation of remote sensing satellites has received considerable attention. The quantitative evaluation method is conventionally based on simulation, but it has a speed-accuracy trade-off. In this paper, a real-time evaluation model architecture for remote sensing satellite clusters is proposed. Firstly, a multi-physical field coupling simulation model of the satellite cluster to observe moving targets is established. Aside from considering the repercussions of on-board resource constraints, it also considers the consequences of the imaging’s uncertainty effects on observation results. Secondly, a moving target observation indicator system is developed, which reflects the satellite cluster’s actual effectiveness in orbit. Meanwhile, an indicator screening method using correlation analysis is proposed to improve the independence of the indicator system. Thirdly, a neural network is designed and trained for stakeholders to realize a rapid evaluation. Different network structures and parameters are comprehensively studied to determine the optimized neural network model. Finally, based on the experiments carried out, the proposed neural network evaluation model can generate real-time, high-quality evaluation results. Hence, the validity of our proposed approach is substantiated. Remote sensing satellites obtain information from the Earth’s surface through optical or microwave payloads . They arThe four main branches of the current quantitative effectiveness evaluation methods are the analytical method, experimental statistical method, multi-index synthesis method, and simulation method . AlthougThe main challenge of using the simulation method to evaluate the remote sensing satellite cluster is the lack of model accuracy . The satRegarding the construction of indicator systems, most remote sensing satellite evaluation indicators only consider the time resolution  and spatial resolution  and percentage of area coverage) ,12. EvenFurthermore, a high-fidelity simulation evaluation model requires heavy computation, especially when the cluster contains numerous satellites . As a coIn this paper, by applying machine learning to the effectiveness evaluation of remote sensing satellite clusters on moving targets, a real-time evaluation model architecture is proposed. Firstly, we establish a multi-physical field coupling simulation model of the satellite cluster, which considers the repercussions of multiple satellite resource constraints and the uncertainty of imaging quality on the observation results. Secondly, we develop an indicator system to evaluate the effectiveness of satellite cluster observations on moving targets. A set of independent indicators is then filtered out through correlation analysis. Thirdly, neural networks are trained with high-fidelity simulation evaluation data. Neural networks of different hidden layers, neurons, and activation functions are trained to determine the optimized model, which can output effectiveness evaluation results in real time.The remainder of the paper is organized as follows. In The scene of the satellite cluster observing the moving targets is illustrated in To evaluate the moving target’s observation effectiveness of the remote sensing satellite cluster, a real-time evaluation model architecture is designed, as shown in The multi-physical field coupling simulation model of remote sensing satellite clusters is described in The simulation condition parameters The moving target observation effectiveness indicator system of the satellite cluster is established in The effectiveness evaluation model based on the simulation result data can be expressed asIn With the rapid forward propagation process of the neural network, the calculation of the satellite cluster effectiveness indicators can be greatly accelerated. The neural network model error of the In this section, the function The forces and moments of celestial bodies, such as Earth, the Sun, and the Moon, are coupled with the satellites’ orbits and attitudes. The ADCS controls the satellite’s position and attitude. They have coupling relationships with the solar array attitude of the POWER, the communication link of the TTC&DT, and the range of the payload’s field of view. Sunlight and the solar array within the POWER are coupled. The remaining battery capacity of POWER is mutually coupled with the power consumed by other subsystems. TTC&DT is coupled with other subsystems by sending and receiving commands and data. The payload’s coverage capability is coupled with the satellite’s orbit and attitude. At the same time, it is also bound by resources, such as power, storage, and data transmission. The payload imaging quality is affected by factors, such as resolution, climate environment, cloudiness level, and lighting conditions, which in turn affect the target detection probability.The typical simulation evaluation modeling only considers a few factors, which are the satellite orbit, attitude, and optical payload visible model. Nonetheless, apart from lacking resource constraint models, such as the power and computer storage, it also does not consider the impact of imaging quality on the mission status. On the basis of the existing models, this paper established three supplement models, which are (1) the power subsystem model, (2) the optical payload’s target detection probability model, and (3) the satellite cluster mission allocation model that considers the entire satellite’s resource constraints.The power subsystem model includes the device power consumption model, the solar array power supply model, and the battery charge and discharge model.(1) The device power consumption model.The total power consumption The on–off state of the devices on board are defined as Denote The power consumption (2) The solar array power-supply model.The output power of the solar array is calculated as follows:The temperature difference (3) The battery charge and discharge model.When the output power of the solar array exceeds the sum of total device power consumption and battery charge power, the regulator consumes the surplus power. The surplus power can be calculated byThe battery is charged only if the output power of the solar array is greater than that of the on-board devices and the current battery capacity is below its maximum value, in which case The battery capacity of The optical payload model consists of two parts: the optical coverage model and the target detection probability model. The coverage state ge model , where iThe resolution of the satellite optical payload isThe 2-D criterion (number cycles) The target static detection probability is defined as (22)PStaThe solar altitude angle is calculated asThe light factor is defined asThe cloud factor is generated by (25)fc=1The visibility factor is generated by (26)fv=eThe target discovery probability can be derived from the product of static detection probability and the other three factors; it can be expressed asAccording to the target detection probability, it is possible to succeed and fail in a single observation mission. Thus, the simulation results may be different, given the same simulation condition. The task status is defined as follows:Finally, the status and the end time of each task are recorded, including the following items:(1) The time series of discovery tasks is recorded as (2) The time series of identification and confirmation tasks is recorded as (3) The time series of successful tracking tasks is recorded as (4) The time series of losing the targets is recorded as The moving target observation mission can be divided into four tasks, which are discovery, identification, confirmation, and tracking. The discovery task refers to the scanning and discovery of regional targets when the potential area of the target is known, but the specific location is not. The identification and confirmation tasks refer to the identification of the target type and model when the approximate target location has been found. The tracking task refers to the continuous tracking of the target when the specific model of the target has been confirmed. The discovery task is generally completed by satellites with a low resolution and a wide field of view. As for the identification, confirmation, and tracking tasks, they are typically accomplished by satellites with a high resolution but a narrow field of view.(1) High-resolution satellites mission allocation model.The mission allocation steps for the identification, confirmation, and tracking tasks are designed as Step 1: obtain the planning current time Step 2: within a planning cycle Step 3: the observation time window of all satellites for all targets during the planning period ndowEnd] .Step 4: the mission profit for each target is presented asStep 5: the total observation cost of each satellite to the observable target is calculated, including the time cost, power consumption cost, and storage consumption cost.The time cost isThe power consumption cost ismaneuver .The storage consumption cost is defined asThe total observation cost is the product of the above three costs, which is expressed asStep 6: allocate the missions to the satellites. The most profitable mission among the remaining missions is chosen and allocated to the satellite that has the lowest cost to fulfill that mission while satisfying the constraints of attitude maneuver, fixed storage, or power supply. The mission’s allocation process loops until all the missions are completed. (2) Wide-swath satellites mission allocation model.The allocation process of the discovery stage is similar to that of the identification, confirmation, and tracking stages mentioned above, with two main differences.The first difference is the target cataloging. As shown in The second difference is the observation profit of the grid target. The profit is calculated as follows:In this section, we propose an evaluation indicator system of moving target observation performance to judge the effectiveness of satellite cluster observation tasks. The evaluation indicator system is screened through correlation analysis to form an independent indicator set. The following describes the methods for developing and filtering the effectiveness evaluation indicator system.The moving target observation effectiveness indicator system considers three abilities: search and discovery, identification and confirmation, and continuous tracking. The details of the effectiveness evaluation indicator system are shown in The concept definition and mathematical description of the effectiveness indicators are as follows:Denote The search and discovery ability.The target discovery probability Search and obtain the time 2.The identification and confirmation ability.The target identification probability Search and obtain the time 3.The continuous tracking ability.The tracking time percentage The average tracking interval The correlation of the indicator system Step 1: calculate the correlation coefficients between the parameters and build the coefficient matrix asStep 2: screen out the highly correlated indicators. If Step 3: calculate the sum of the linear correlation coefficients of indicators Step 4: compare the values of Step 5: continue Steps 2 to 4, until the indicator system is screened and formed.The refining of simulation model granularity enhances the simulation accuracy, but diminishes the simulation efficiency. Hence, it is challenging to meet the efficiency requirement of both the iterative optimization at the designer end and the real-time decision making at the user end. To provide a solution to this problem, a backpropagation (BP) neural network model is designed and trained for user stakeholders to realize the rapid evaluation of satellite cluster effectiveness. This section introduces sample generation and neural network training in detail.Each sample has an input and an output. The sample input is defined asThe sample output is the effectiveness indicator, which can be expressed as followsA single-time simulation has high uncertainty resulting from the uncertainty of the random target initial parameters and the existence of the detection probability. Therefore, in order to generate reliable samples, each simulation case is performed multiple times to obtain the statistical value of the effectiveness indicators.Denote The neural network training process is divided into three different stages, which are the sample set division, network training, and performance testing, as shown in To begin, the sample sets Next, deep neural networks are trained in two steps, starting with the search for the best activation function, followed by the selection of the optimal network parameters. In the first step, a single hidden layer neural network traverses a set of activation functions to find several optimal activation functions. Those activation functions are softmax, tansig, logsig, elliotsig, poslin, purelin, radbas, satlin, satlins, and tribas. In the second step, a multi-hidden-layer neural network is implemented to determine the best combination of network structures and parameters. The neural network traverses the various combinations of network structures and parameters, including the number of hidden layers, the number of neurons in each layer, and the activation functions found in the first step.Finally, the neural networks are tested on the training set and test set. Denote The average error of the training and test sets is defined asIn this section, the ship target observation scene is selected for the experiments. Firstly, we compared the proposed model with the model mentioned in the reference ,9. This The remote sensing satellite cluster is designed according to the Walker Constellation of solar synchronous orbit, which is composed of wide-swath satellites and high-resolution satellites. Each orbital plane of the cluster contains ten satellites with an interval phase of 36°. The wide-swath satellites lie in the first and sixth positions of each orbital plane, whereas the high-resolution satellites lie in the other eight positions. The satellite cluster configuration is shown in The key parameters of the satellite cluster are shown in The mission area for this experiment is restricted to the Pacific, from 130° E 10° N to 150° E 30° N. The ships are initialized within a random region of 2° × 2°. The positions, velocities, and course angles are also arbitrarily selected. The ship size used here is 155.3 m × 20.4 m. The simulation starts at 0:00 a.m. on one day in 2021 and lasts for 21,600 s. The randomly generated environmental parameters used for the model comparison are shown in The initial information of ten arbitrary ships is presented in We built three simulation models of different granularity according to refs. ,9, and oThe simulation process of satellite cluster observing the moving target is shown in The calculated effectiveness evaluation indicators of the three model granularities are presented in The improvement of the model fidelity leads to a decrement in computational efficiency. The simulations of the three model granularities were executed on a computer with Windows 10 and Intel i7-9700 @ 3.0 GHz CPU. The time taken to perform a single simulation under the three granularities was recorded, as shown in A large quantity of sample data is required for both screening the indicator system and training the neural network. In accordance with the stakeholders’ requirements, data points are randomly scattered taking the mission and environmental parameters as the sample input. The range of the sample input task parameters is shown in We randomly generated 1000 samples as the inputs. For each sample, a set of 20 random simulation parameter combinations were created. The parameters included the position, velocity between 0 and 10 m/s, and course angle between 0° and 360°. After 1000 × 20 = 20,000 simulations, 1000 samples were produced. Subsequently, the effectiveness indicator calculations were carried out on the samples. The first ten samples are shown in The initial evaluation indicator system of the moving target observation isWe computed the correlation coefficients between the indicators. The correlation matrix A is presented below.The correlation coefficient of the discovery probability The sum of the correlation coefficient between From a comparison of Likewise, the correlation coefficient of the identification probability The sum of the correlation coefficient between From a comparison of As a result, the evaluation indicator system contains four indicators, which are the discovery probability A total of 1000 samples were randomly shuffled and divided into a training set of 800 samples and a test set of 200 samples. A single hidden layer network structure was used in the traversal of activation functions mentioned in The five best activation functions were found, which are softmax, poslin, satlin, tansig, and logsig. Then, the multi-layer network structure and parameters were tested, including the number of hidden layers (2/3/4), the number of neurons in each layer (20–200), and the five best activation functions. The best neural network of each effectiveness indicator is summarized in The training and test results are illustrated in The time consumption of the effectiveness evaluation generated by the simulation method and the neural network model is shown in The simulation model in the architecture is a multi-physical field coupled. Apart from considering the repercussion of on-board resource constraints, it also considers the consequence of imaging’s uncertainty on the observation results. As compared with our proposed model granularity, the traditional coarse-grained model has a maximum error of more than 60%, which proves the effectiveness of the proposed model’s granularity.A moving target observation effectiveness indicator system of the satellite clusters is established. In comparison with the current indicator system that contains accessibility indicators, such as coverage and resolutions, our proposed indicators can better reflect the effectiveness of the operation process. Moreover, we screened the indicator system through model-based quantitative analysis. This not only reduces the redundancy of the indicator system developed by the domain experts, but also increases the indicator system’s hierarchy and independence.The neural network model can be trained in the architecture to evaluate the effectiveness with real-time computation. The architecture supports the finding of the best network structure and parameters, including the number of hidden layers, as well as the number of neurons and activation function in each layer. The result indicates that the neural network model not only achieves high accuracy on the training set, but also generalizes well on the test set. The conflict between the accuracy and speed is therefore resolved.Remote sensing satellite clusters are usually comprised of wide-swath satellites and high-resolution satellites. They are playing an increasingly important role in the remote sensing field owing to their ability to complete census and detailed survey tasks for various targets. Due to the high cost of development and operation, remote sensing satellite clusters require quantitative effectiveness evaluation to support decision making in their entire life cycle. The effectiveness evaluation is usually based on simulation, but there is a conflict between accuracy and speed. Thus, the main goal of this paper was to present an architecture to achieve real-time high-quality effectiveness evaluation of remote sensing satellite clusters. The significant advantages of the architecture are as follows:Our suggested method is not merely applicable to the effectiveness evaluation of remote sensing satellite clusters, but also single satellites and other types of satellites. Yet, our method has no obvious advantage over the coarse-grained model when only low accuracy is required. A coarse-grained model is more advantageous as it already has high computational efficiency. On the contrary, the sample creation method that we mentioned demands high computational resources.Future research will be undertaken in two aspects, which are increasing the sample size and sample quality. This could be useful in reducing the computational resources needed in neural network training and enhancing the accuracy of the neural network. Regarding the sample size issue, due to the limitation of computing resources, only 1000 samples are created to be applied in our network training process. The error of networks might be reduced if more training samples are generated. In the future, the simulation model can be further optimized to acquire as many samples as possible with the same computational resources. For example, the relationship between effectiveness and model granularity can be thoroughly investigated, so as to refine the granularity of the important parts and reduce the granularity of the less important ones. In terms of sample quality, the noise in the samples will cause errors in the network evaluation results. It is a worthwhile research direction in the future to improve the training effect by preprocessing the samples.


Reporting effect size index estimates with their confidence intervals (CIs) can be an excellent way to simultaneously communicate the strength and precision of the observed evidence. We recently proposed a robust effect size index (RESI) that is advantageous over common indices because it’s widely applicable to different types of data. Here, we use statistical theory and simulations to develop and evaluate RESI estimators and confidence/credible intervals that rely on different covariance estimators. Our results show (1) counter to intuition, the randomness of covariates reduces coverage for Chi-squared and F CIs; (2) when the variance of the estimators is estimated, the non-central Chi-squared and F CIs using the parametric and robust RESI estimators fail to cover the true effect size at the nominal level. Using the robust estimator along with the proposed nonparametric bootstrap or Bayesian (credible) intervals provides valid inference for the RESI, even when model assumptions may be violated. This work forms a unified effect size reporting procedure, such that effect sizes with confidence/credible intervals can be easily reported in an analysis of variance (ANOVA) table format.The online version contains supplementary material available at 10.1007/s11336-022-09899-x. They are unaffected by the sample size and play an important role in power analyses, sample size planning and meta-analyses  .The goal of this paper is to establish an accurate interval estimation procedure for the RESI and establish a framework for the analysis of effect sizes based on the RESI. Because the RESI is related to the NCP of the Chi-squared statistic, an intuitive approach is to use existing methods based on non-central distributions to construct intervals for the NCP of a noncentral Chi-squared or F distribution  White, . First, SI using . These df(n), g(n) are said to be asymptotically equivalent if To compare the sampling distributions of the oracle, parametric, and robust test statistics under the alternative, in this section, we compare the variance of the test statistics under linear regression analytically. When the covariance of entclass1pt{minimaribution  will faiX is full rank and Throughout this section, we assume Oracle Estimator:  Assuming known covariance, the test statistic ^2 n-m-}\left not equal to 0. Since the squared RESI was defined as the NCP divided by sample size Suppose the test statistics from the sample u) of central u) bounds for the NCP of the F distribution can be determined from the observed statistic, u to satisfyhttps://cran.r-project.org/web/packages/RESI/index.html).Our code is adapted from the code in MBESS, Kent & Hainsworth . In this case, the values in the binary variable in a bootstrapped dataset may be completely identical and the nonparametric method will fail due to rank deficiency, whereas Bayesian bootstrap is still functional given such analysis data. In addition, the resulting credible intervals provide Bayesian interpretation.W denote the dataset and the S . Then, the RESI and corresponding CIs are estimated to indicate the effect size of each factor after controlling others. The results are summarized in an ANOVA table format  patients, we fit a logistic regression model to study the effect of psychosis on relational memory compared to healthy controls Table . Resultsentclass1pt{minimap-values are sample size dependent, the RESI can be compared across the four models that come from two different samples and two model types.The RESI also makes it easy to compare findings across these two studies that have different sample sizes. The effect sizes are both large and similar in magnitude across the two studies indicating a similar relational memory deficit in EP and SZ. While the test statistics and In the previous sections, we proposed three estimators and several ways of constructing (confidence/credible) intervals for the RESI. In this section, we use 1,000 simulations to evaluate the performance of the proposed interval construction procedures with respect to each estimator under different scenarios. We evaluate the influence of heteroskedasticity, data skewness and fixed/random covariate(s) on the performance of estimators and intervals in some particular situations that we believe represent reasonable conditions researchers care about in practice. All intervals are constructed at the 95% confidence/credible level.We simulate a simple linear regression model X is treated as fixed or random, the values of the covariate were generated in two ways: (1) when the covariate is fixed, n individuals have their covariate with value 1 and the remaining have value 0; (2) when the covariate is random, To illustrate the difference in the performance of the intervals between an experimental and observational design where the covariate(s) We ran simulations to assess the bias of the estimators and the coverage of the confidence/credible intervals. We considered eight possible cases where there are homo- or heteroskedastic errors, symmetric or skewed errors, and fixed or random covariates in each simulation.S  and used them to to easily report effect sizes and their CIs in an ANOVA table format. We proposed 3 different estimators for the RESI and a variety of ways to construct intervals. The oracle estimator assumes the covariance matrix is known, so is not possible to compute in applications. Through simulations, we showed that all 3 estimators are consistent under homoskedasticity and the robust estimator is consistent under heteroskedasticity. We used statistical theory and simulations to demonstrate that the non-central Chi-squared CI has low coverage when the covariance in the Wald test statistic needs to be estimated. In addition, the randomness of covariate(s) also reduces the coverage of Chi-squared and F CIs, which is an important implication for observational studies where the covariates are random instead of fixed/controlled by the experimenters. According to the simulation results, using the robust estimator along with the (nonparametric) bootstrap CI is generally most accurate and applicable to conduct consistent estimation and valid inference using the RESI. The credible interval has comparable performance to the nonparametric bootstrap CI for the robust estimator. It also has a Bayesian interpretation and can be used as an alternative to the nonparametric bootstrap CI.p-values. This approach may help to address the limitations of null hypothesis significance testing . It outputs the estimated RESI along with intervals in a summary table, with which researchers can conveniently report their observed results. Coupled with the generality of the RESI, researchers studying the same scientific questions but using different types of data can easily communicate their observed effect sizes and confidence/credible intervals instead of having to translate between different effect size indices.We also provided functions to estimate RESI and its CI in our RESI R package  requires further research.Supplementary file 1 (pdf 1557 KB)Below is the link to the electronic supplementary material.


Electrospinning has gained much attention in recent years due to its ability to easily produce high-quality polymeric nanofibers. However, electrospinning suffers from limited production capacity and a method to readily scale up this process is needed. One obvious approach includes the use of multiple electrospinning needles operating in parallel. Nonetheless, such an implementation has remained elusive, partly due to the uneven electric field distribution resulting from the Coulombic repulsion between the charged jets and needles. In this work, the uniformization of the electric field was performed for a linear array of twenty electrospinning needles using lateral charged plates as auxiliary electrodes. The effect of the auxiliary electrodes was characterized by investigating the semi-vertical angle of the spun jets, the deposition area and diameter of the fibers, as well as the thickness of the produced membranes. Finite element simulation was also used to analyze the impact of the auxiliary electrodes on the electric field intensity below each needle. Implementing parallel lateral plates as auxiliary electrodes was shown to help achieve uniformization of the electric field, the semi-vertical angle of the spun jet, and the deposition area of the fibers for the multi-needle electrospinning process. The high-quality morphology of the polymer nanofibers obtained by this improved process was confirmed by scanning electron microscopy (SEM). These findings help resolve one of the primary challenges that have plagued the large-scale industrial adoption of this exciting polymer processing technique. Polymeric nanofibers are seeing increased application in a wide range of fields, ranging from drug delivery to sensors and advanced filtration . In manyNumerous researchers have tried to address these electric field issues using varying needle arrangements  and needIn the present work, a linear array of twenty needles was used to scale up the electrospinning process. As expected, the electrospinning jets were found to interact with one another, resulting in non-uniform fiber diameter and membrane thickness. For most applications, the uniformity of the fiber diameter and the thickness of the deposited non-woven fiber membrane are critical parameters determining the quality of the material produced ,19. We fPolyvinylidene difluoride (PVDF) powder from Arkema  with the trade name Kynar 741 was dissolved in a solvent composed of 70% n,n-dimethylformamide (DMF) and 30% acetone , to obtain an 18%wt PVDF solution, as reported in a previous work . The mixA Hitachi S3600  scanning electron microscope (SEM) was used to assess the fibers. ImageJ software 1.53a  was used to measure the diameter of the fibers; 100 measurements were made per sample on three different images and the standard deviation is reported.D is the electric displacement field, E is the electric field, and V is the electric potential. The minimum and maximum free tetrahedral element size was 0.003 and 1 mm, respectively. In the simulation, stainless steel chromium steel, from the COMSOL library, was used as the material for the needles, air for the space domain, and pure aluminum for the collector.COMSOL Multiphysics 5.6  was used to model the intensity of the electric field just below the needles. To do so, both Gauss’ and Faraday’s Laws (Equations (1) and (2)) were solved using the electrostatic stationary physics interface. In these equations, A representative in situ image showing the multi-needle electrospinning apparatus is shown in As indicated by the orange arrows in The deposition pattern shows a well-defined middle section, where the deposition spots are uniform in size and density, and two distinct side sections, where the deposition spots are spread over a wider area, which decreases the deposition density of the fibers.The resulting fibers were analyzed by SEM and representative images of the fibers are shown in Using a conveyor belt as a collector, large membranes were produced, and the thickness was measured along the width, as presented in These deposition non-uniformities are due to electric field interaction between the needles. A theoretical explanation for this phenomenon is presented in the discussion section.This issue was resolved using parallel plate auxiliary electrodes: a folded aluminum foil sheet was positioned on each side of the needle array, both connected to the same voltage source as the needles (15 kV with −15 kV at the collector). This improved setup is presented in After the introduction of the parallel plate auxiliary electrodes, the deposition pattern no longer shows distinctive middle and side sections. It is at this point relevant to mention that, since most scaled-up electrospinning systems use either a conveyor belt or a rotative drum as the collector, fiber deposition will be uniform over both the width and the length of the produced membranes, due to the motion of the collector. Representative SEM images are shown in Using this optimized fiber-producing technique that includes the parallel plate auxiliary electrodes, large nonwoven polymer nanofiber membranes were electrospun using a conveyor as the collector, as shown in To better understand the experimental observations, the electric field intensity across the needle array was computed using COMSOL. To do so, a 3D geometry was used, as presented in Perhaps counterintuitively, the electric field near each needle depends greatly on the relative position of the needle along the needle array, as illustrated in This non-uniformity of the electric field due to the interaction between the needles explains the non-uniformities observed experimentally: indeed, during the electrospinning process, the electric field is responsible for pulling the polymeric jets out from the needles. An inhomogeneous electric field will, therefore, result in unstable jets pulled at a deflected angle: the jets on the sides are pushed outward and are more dispersed, unlike the jets in the middle, that are constrained by their nearest neighbors.It is well known in the literature that a stronger electric field causes a stronger stretching of the polymeric jet, resulting in smaller fiber diameters . This exTo resolve these issues, parallel plate auxiliary electrodes were implemented, as presented in the experimental section. This improved setup was modeled by adding lateral conducting plates that were subjected to the same voltage as the needles, as shown in the new geometry presented in As illustrated in Experimentally, it was found that the auxiliary electrode plates had to be made a little longer (approximatively 3.5 cm) to compensate for the Coulombic repulsion of the charged jets. Since any electrical charge on the polymeric jets was unaccounted for in the equilibrium-state finite element model, this was to be expected.When properly optimized, the lateral auxiliary electrode plates repel the side jets with the same strength as the other needles, realigning the jets toward the collector and eliminating the deflection angle. Since all the electrospinning jets are now vertically oriented directly toward the collector, the fibers are deposited uniformly. We, therefore, find that the uniformity of the electric field obtained across the needles using parallel auxiliary electrode plates creates the necessary stable processing conditions for the production of high-quality fibers of homogeneous diameter.Parallel plate auxiliary electrodes have, therefore, proven to be a simple and efficient way to optimize the multi-needle electrospinning process. The simulation results were found to be in agreement with the experimental results. Since no semi-vertical jets were observed, a uniform deposition pattern was produced. Since fiber diameter along the needle array was found to be uniform, it can be, reasonably, concluded that the electric field is quite uniform, which indicates steady processing conditions.The use of a multi-needle apparatus seems an obvious way to scale up the electrospinning process to increase production yields. However, as numerous researchers have found, such an implementation suffers from unstable processing parameters due to the non-uniformity of the electric field, resulting in the deflection of electrospinning jets that cause heterogenous fiber deposition and membrane thickness, as well as poor fiber diameter control.We found that this challenge can be readily overcome by introducing parallel auxiliary electrode plates that confine the needle array. Experimental observations and finite element computational models confirm that the introduction of these additional electrodes achieves uniformity of the electric field along the needle array. This greatly improves the process parameter controls, thereby enabling the scaled-up high-yield production of good quality, homogeneous polymeric nanofibers using the electrospinning technique.