Small Number Of Samples Research Articles

Comparative study on the quality control effectiveness of AI-PBRTQC and traditional PBRTQC model in identifying quality risks.

We compared the quality control efficiency of artificial intelligence-patient-based real-time quality control (AI-PBRTQC) and traditional PBRTQC in laboratories to create favorable conditions for the broader application of PBRTQC in clinical laboratories. In the present study, the data of patients with total thyroxine (TT4), anti-Müllerian hormone (AMH), alanine aminotransferase (ALT), total cholesterol (TC), urea, and albumin (ALB) over five months were categorized into two groups: AI-PBRTQC group and traditional PBRTQC group. The Box-Cox transformation method estimated truncation ranges in the conventional PBRTQC group. In contrast, in the AI-PBRTQC group, the PBRTQC software platform intelligently selected the truncation ranges. We developed various validation models by incorporating different weighting factors, denoted as λ. Error detection, false positive rate, false negative rate, average number of the patient sample until error detection, and area under the curve were employed to evaluate the optimal PBRTQC model in this study. This study provides evidence of the effectiveness of AI-PBRTQC in identifying quality risks by analyzing quality risk cases. The optimal parameter setting scheme for PBRTQC is TT4 (78-186), λ = 0.03; AMH (0.02-2.96), λ = 0.02; ALT (10-25), λ = 0.02; TC (2.84-5.87), λ = 0.02; urea (3.5-6.6), λ = 0.02; ALB (43-52), λ = 0.05. The AI-PBRTQC group was more efficient in identifying quality risks than the conventional PBRTQC. AI-PBRTQC can also effectively identify quality risks in a small number of samples. AI-PBRTQC can be used to determine quality risks in both biochemistry and immunology analytes. AI-PBRTQC identifies quality risks such as reagent calibration, onboard time, and brand changes.

A novel fault diagnosis method for Bayesian networks fusing models and data

Data-driven methods are the mainstream methods applied in current fault diagnosis research; however, in natural mechanical systems, fault samples are difficult or even impossible to obtain, and physical model-based methods cannot easily model the faults of complex systems. To address the respective limitations of data-driven and model diagnosis methods in fault diagnosis, in this paper, a Bayesian network fault diagnosis method based on a joint model and data modelling is proposed. Conditional Gaussian Bayesian networks are used to fuse discrete and continuous information, and the discrete information in the residuals is used to compensate for the insufficient feature information problem caused by small amounts of data. To address the exponential modeling complexity growth in the case of multiple fault types cases, a single classification method is chosen for fault classification. Meanwhile, the T2 statistics distance suppression method is introduced into the Bayesian network to improve the diagnostic capabilities for new types of faults. By comparing the mainstream methods that address small sample diagnosis and the existing fusion methods, the experimental results show that in cases with small numbers of samples, the method in this paper has good fault diagnosis capabilities by effectively utilizing the model’s diagnostic results to compensate for the diagnostic errors caused by a lack of data, and it has an improved ability to recognize unknown faults.

A dimension reduction-based Kriging modeling method for high-dimensional time-variant uncertainty propagation and global sensitivity analysis

Surrogate models have been widely used in the uncertainty propagation and global sensitivity analysis of complex evaluation-expensive engineering problems. However, the construction of high-accuracy surrogate models for time-variant problems with high-dimensional inputs and outputs using a small number of training samples remains a challenge. To address this challenge, we propose a dimension reduction-based Kriging modeling (KMDR) method for high-dimensional time-variant uncertainty propagation and global sensitivity analysis. Singular value decomposition is performed on the original time-variant response to extract principal components from high-dimensional outputs. And the improved sufficient dimension reduction (ISDR) is performed on high-dimensional inputs to identify the latent input space with respect to each principal component of outputs. A ladle estimator with a rigorous mathematical definition is then employed to determine the number of principal components of the outputs and the dimensionalities of the latent input spaces. The ladle estimator considers variabilities in both eigenvalues and eigenvectors of a matrix and can determine the latent dimensionality more accurately and efficiently than existing approaches. Subsequently, Kriging models between high-dimensional inputs and each principal component of the outputs are constructed based on a newly devised Kriging kernel, which embeds the information of the ISDR into the kernel function and can achieve higher accuracy than directly constructing Kriging models between latent inputs and outputs. In addition, a generalized variance-based sensitivity index, which can quantify the effects of the inputs on the overall time-variant response, is defined and computed directly from Sobol’ sensitivity indices of the inputs at each time node. Finally, the surrogate model of the time-variant system constructed by the KMDR is directly adopted for efficient time-variant uncertainty propagation and global sensitivity analysis. Several examples demonstrate that the proposed approach can construct more accurate surrogate models and obtain more accurate time-variant uncertainty propagation and global sensitivity analysis results than existing methods with a small training set.

Fault diagnosis of power equipment based on variational autoencoder and semi‐supervised learning

SummaryThe issue of fault diagnosis in power equipment is receiving increasing attention from scholars. Due to the important role played by bearings in power equipment, bearing faults have become the main factor causing the shutdown of wind turbines units. Therefore, this paper takes bearing equipment as an example for research. In order to solve the problem of insufficient and unbalanced fault sample data of wind turbines bearings, a fault diagnosis (FD) method based on variational autoencoder and semi‐supervised learning is proposed in this paper. Firstly, based on Label Propagation‐random forests (LP‐RFs) and a small number of labeled fault samples, a semi‐supervised learning algorithm is proposed to label the original data samples. Secondly, a small number of training samples are preprocessed by the variational autoencoder to reduce the imbalance of the fault samples. Then, the RFs‐based method is adopted to train the processed fault samples to obtain a mature FD classifier. Finally, the proposed method is applied to FD for bearings, and the results show that the proposed method can realize bearings fault diagnosis (BFD). And meanwhile, the proposed method can also be applied for fault diagnosis in power transmission and transformation systems.

Revealing heterogeneity in chronic lymphocytic leukemia: AI-driven insights into aggressive and indolent disease subtypes.

e19029 Background: Chronic lymphocytic leukemia (CLL) exhibits a broad spectrum of clinical behaviors, from indolent courses requiring minimal intervention to aggressive forms demanding immediate treatment. The underlying heterogeneity of CLL poses significant challenges in predicting disease progression and tailoring patient-specific therapeutic strategies. Leveraging advanced and novel artificial intelligence (AI) and machine learning (ML) methodologies, our study aims to dissect the complexity of CLL, focusing on the identification and characterization of biomarkers that differentiate aggressive and indolent disease forms. Methods: The NetraAI is a priorietaryAI/ML system that is also being utilized in a research collaboration at a leading American oncology institute (not disclosed) that integrates machine intelligence, augmented intelligence, and an agent that interacts with AI-derived patient representations. This system was designed to learn from datasets with a small number of samples and many independent variables. This system operates by generating hypotheses in the form of interactive representations of patient populations that reveal heterogeneity along with statistically significant driving factors. Finally, an automated agent called AutoPlay interacts with these representations and generates an accounting of all variables, subpopulations of samples, and it also generates a human readable perspective on what was discovered by the data set. Results: We applied the NetraAI to a CLL dataset and used it to refine the analysis and interpretation of the corresponding transcriptomic data (GSE39411). Significantly lower levels of FADS3, GSDME, LPL, IMMT, NMB, and AEBP1 and higher expressions of COBLL1, P2RY1R, PDE8A, SYNE2, and FCRL3 were identified as hallmarks of indolent CLL, suggesting these markers could inform less aggressive disease management strategies. Furthermore, within a total of 104 CLL samples, we uncovered two distinct subpopulations within aggressive CLL, delineated by unique genetic markers. Group A, encompassing 31 aggressive CLL samples is predominantly characterized by the expression of lipoprotein lipase (LPL), while Group B, comprising 22/23 aggressive samples, is characterized by ZBTB20 and SYNE2. Our poster will also provide glimpses into a more refined heterogeneity that emerged from the analysis. Our results not only underscore the heterogeneity within aggressive CLL, but also highlights the potential for these markers to guide prognostic assessments and therapeutic decisions. Conclusions: This study not only advances our understanding of CLL heterogeneity, but also sets the stage for the development of more personalized and effective treatment modalities. By identifying drivers of disease aggression and indolence, we pave the way for targeted therapies that could significantly improve patient outcomes in CLL.

Research and analysis on fault diagnosis of multistage centrifugal pump for mining

Abstract The vibration data collected by the fault experiment are analyzed in the time domain and frequency domain respectively, and the conclusion shows that the frequency characteristics of some vibration data cannot be fully characterized due to noise interference. In order to solve the problem of noise interference, this paper uses the VMD algorithm based on the kurtosis peak to denoise the collected data, constructs wavelet time-frequency map samples through the wavelet transform method to provide input sources for the subsequent convolutional neural network, and uses the image samples for fault diagnosis and identification. [1] Through the convolutional neural network built to diagnose and identify the different states of the multistage centrifugal pump, it can be seen that the recognition accuracy, training time, and recognition time of a single sample of various image samples in the three cases of no noise reduction, noise reduction and a small number of samples can be found through the method of comparative analysis, and compared with the traditional SVM method, the superiority of the convolutional neural network using image recognition can be found.

ASESSMENT OF THE EFFECTIVENESS OF CAVITATION PROCESSING OF DARK OIL PRODUCTS

The paper presents experimental results of the influence of the conditions of cavitation treatment of dark petroleum products in the hydrodynamic regime on the yield of fractions boiling up to 400 °C. Straight-run products (fuel oils, vacuum gas oils), as well as catalytic cracking gas oils, were considered as raw materials. The influence of raw material characteristics (density, fractional and group hydrocarbon composition) and its processing conditions (pressure, at five cycles of exposure) on the yield of the target fraction is considered. The linear dependence of the yield of the target fractions on the processing pressure is revealed. The constructed regression models are adequate, informative, and all their coefficients are significant. Taking into account the density of the feedstock did not improved models. The best model constructed was tested using the cross-validation method. The average forecast error was 11%, the maximum was 22%. For a small number of samples, such an error is acceptable. The possibility of assessing the effectiveness of cavitation treatment of crude oil based on data on its fractional and group hydrocarbon composition is shown. It was possible to build several regression models linking the increase in yield with pressure during processing and the content of resins and oils in the raw materials. All of them are adequate and informative, and their coefficients are significant. The increase in the yield of fractions boiling up to 400 °C is associated with the transformations of hydrocarbons during cavitation. The possibility of evaluating the effectiveness of cavitation treatment of petroleum raw materials based on data on its group hydrocarbon composition has been established. Several regression models have been constructed linking the increase in yield with the pressure during processing and the content of resins and oils in the raw materials. All of them are adequate and informative, and their coefficients are significant. Based on the constructed models, it is assumed that the change in the fractional composition of petroleum products during cavitation treatment is more associated with the destruction of the transition and solvate layers of complex structural units of the oil dispersed system than with the course of cracking reactions. For citation: Peshnev B.V., Burlyaeva E.V., Nikishin D.V., Nikolaev A.I., Kuznetsov A.S. Asessment of the effectiveness of cavitation processing of dark oil products. ChemChemTech [Izv. Vyssh. Uchebn. Zaved. Khim. Khim. Tekhnol.]. 2024. V. 67. N 7. P. 103-110. DOI: 10.6060/ivkkt.20246707.7006.

Diagnosis of Sliding Bearing Lubrication State Based on SC-ResNet

In response to the issue of poor diagnostic performance of models due to the scarcity of samples for lubrication failure states in sliding bearings in practical engineering applications, this thesis proposes a fault diagnosis method for the lubrication state of sliding bearings based on a Self-Calibrated Residual Network (SC-ResNet). By leveraging the self-calibrated convolution, which significantly enhances the network's receptive field and feature extraction capabilities without increasing the number of parameters and complexity, a self-calibrated residual block is designed to construct the SC-ResNet model. This model is capable of diagnosing the lubrication state of bearings using a small number of samples inputted into a pre-trained model. Experimental results indicate that this method performs exceptionally well under conditions with a limited number of samples, achieving higher Recall and F1-score values compared to other methods.

The impact of Ethiopian Green Legacy Initiative (GLI) on landscape functionality and plant species diversity in Lake Hawassa watershed, Ethiopia

AbstractAs one of the responses to the global commitments against climate change, the Ethiopian Government launched a nationwide Green Legacy Initiative (GLI) in 2019, which largely focused on forest tree plantations with some inclusion of fruit trees. Despite its tremendous efforts and investments, its effectiveness and impacts have not been studied. This paper attempted to address this necessity by conducting a cross‐sectional quasi‐experiment in three randomly selected woredas/districts of Lake Hawassa Watershed from August 20 to September 2, 2023. The research hypothesized the likely impacts of GLI on four dependent variables (hydrological regulation, soil stability, nutrient cycling and plant species diversity). To achieve this, the research considered the two variants of GLI practices (plantation with and without soil and water conservation measures) and the corresponding control sites. Having three sites and three treatments with five replications, the study involved a total of forty‐five quadrats of the same size (20 m × 20 m). The first three parameters were analysed using the landscape functionality analysis method, while the fourth employed Shannon's diversity index. Results of ANOVA showed that, on average 87% of randomly selected quadrats were found to significantly improve the local hydrology (runoff potential) (≈ 83.3% with Av. p = 0.012), soil stability (≈100% with Av. p = 0.002), nutrient cycling (≈83.3% with Av. p = 0.017) and plant species diversity (≈83.3% with Av. p = 0.012). The research revealed positive results from the Ethiopian Green Legacy Initiative. The small number of samples is acknowledged as a limitation of the research.

A Few-Shot Object Detection Method for Endangered Species

Endangered species detection plays an important role in biodiversity conservation and is significant in maintaining ecological balance. Existing deep learning-based object detection methods are overly dependent on a large number of supervised samples, and building such endangered species datasets is usually costly. Aiming at the problems faced by endangered species detection, such as low accuracy and easy loss of location information, an efficient endangered species detection method with fewer samples is proposed to extend the few-shot object detection technique to the field of endangered species detection, which requires only a small number of training samples to obtain excellent detection results. First, SE-Res2Net is proposed to optimize the feature extraction capability. Secondly, an RPN network with multiple attention mechanism is proposed. Finally, for the classification confusion problem, a weighted prototype-based comparison branch is introduced to construct weighted category prototype vectors, which effectively improves the performance of the original classifier. Under the setting of 30 samples in the endangered species dataset, the average detection accuracy value of the method, mAP50, reaches 76.54%, which is 7.98% higher than that of the pre-improved FSCE method. This paper also compares the algorithm on the PASCOL VOC dataset, which is optimal and has good generalization ability compared to the other five algorithms.

Reconstructing the sound speed profile of South China Sea using remote sensing data and long short-term memory neural networks

IntroductionSound waves are refracted along the direction of their propagation owing to spatial and temporal fluctuations in the speed of sound in seawater. Errors are compounded when sound speed profiles (SSPs) with low precision are used to detect and locate distant underwater targets because an accurate SSP is critical for the identification of underwater objects based on acoustic data. Only sparse historical spatiotemporal data on the SSP of the South China Sea are available owing to political issues, its complex atmospheric system, and the unique topography of its seabed, because of which frequent oceanic movements at the mesoscale affect the accuracy of inversion of its SSP.MethodIn this study, we propose a method for the inversion of the SSP of the South China Sea based on a long short-term memory model. We use continuous-time data on the SSP of the South China Sea as well as satellite observations of the height and temperature of the sea surface to make use of the long-term and short-term memory-related capacities of the proposed model.ResultIt can achieve highly accurate results while using a small number of samples by virtue of the unique structure of its memory. Compared with the single empirical orthogonal function regression method, the inversion accuracy of this model is improved by 24.5%, and it performed exceptionally well in regions with frequent mesoscale movements.DiscussionThis enables it to effectively address the challenges posed by the sparse sample distribution and the frequent mesoscale movements of the South China Sea.

Evaluation of Cordyceps sinensis Quality in 15 Production Areas Using Metabolomics and the Membership Function Method.

Cordyceps sinensis is a precious medicinal and edible fungus, which is widely used in body health care and disease prevention. The current research focuses on the comparison of metabolite characteristics between a small number of samples and lacks a comprehensive evaluation of the quality of C. sinensis in a large-scale space. In this study, LC-MS/MS, principal component analysis (PCA), hierarchical cluster analysis (HCA), and the membership function method were used to comprehensively evaluate the characteristics and quality of metabolites in 15 main producing areas of C. sinensis in China. The results showed that a total of 130 categories, 14 supercategories, and 1718 metabolites were identified. Carboxylic acids and derivatives, fatty acyls, organo-oxygen compounds, benzene and substituted derivatives, prenol lipids, and glycerophospholipids were the main components of C. sinensis. The HCA analysis and KEGG pathway enrichment analysis of 559 differentially accumulated metabolites (DAMs) showed that the accumulation models of fatty acids and conjugates and carbohydrates and carbohydrate conjugates in glycerophospholipid metabolism and arginine and proline metabolism may be one of the reasons for the quality differences in C. sinensis in different producing areas. In addition, a total of 18 biomarkers were identified and validated, which had a significant discrimination effect on the samples (p < 0.05). Overall, YS, BR, and ZD, with the highest membership function values, are rich and balanced in nutrients. They are excellent raw materials for the development of functional foods and provide scientific guidance for consumers to nourish health care.

Optimizing Word Embeddings for Patient Portal Message Datasets with a Small Number of Samples.

Patient portal messages often relate to specific clinical phenomena (e.g., patients undergoing treatment for breast cancer) and, as a result, have received increasing attention in biomedical research. These messages require natural language processing and, while word embedding models, such as word2vec, have the potential to extract meaningful signals from text, they are not readily applicable to patient portal messages. This is because embedding models typically require millions of training samples to sufficiently represent semantics, while the volume of patient portal messages associated with a particular clinical phenomenon is often relatively small. We introduce a novel adaptation of the word2vec model, PK-word2vec, for small-scale messages. PK-word2vec incorporates the most similar terms for medical words (including problems, treatments, and tests) and non-medical words from two pre-trained embedding models as prior knowledge to improve the training process. We applied PK-word2vec on patient portal messages in the Vanderbilt University Medical Center electric health record system sent by patients diagnosed with breast cancer from December 2004 to November 2017. We evaluated the model through a set of 1000 tasks, each of which compared the relevance of a given word to a group of the five most similar words generated by PK-word2vec and a group of the five most similar words generated by the standard word2vec model. We recruited 200 Amazon Mechanical Turk (AMT) workers and 7 medical students to perform the tasks. The dataset was composed of 1,389 patient records and included 137,554 messages with 10,683 unique words. Prior knowledge was available for 7,981 non-medical and 1,116 medical words. In over 90% of the tasks, both reviewers indicated PK-word2vec generated more similar words than standard word2vec (p=0.01).The difference in the evaluation by AMT workers versus medical students was negligible for all comparisons of tasks' choices between the two groups of reviewers (p = 0.774 under a paired t-test). PK-word2vec can effectively learn word representations from a small message corpus, marking a significant advancement in processing patient portal messages.

Explainable machine learning models for defects detection in industrial processes

Machine learning algorithms in non-linear pattern recognition for defect detection in manufacturing processes are increasingly prevalent in the context of Industry 4.0. This scenario enables factories to develop methodologies for monitoring and controlling the quality of their products, obtaining better operational efficiency indicators, and offering increasingly competitive products in the market. Explainable Artificial Intelligence can facilitate understanding how the models make decisions and assist in tracking anomaly points. This paper proposes an explainability framework for machine learning models in defect detection. This work applied the multinational industry’s approach to the tire manufacturing process. Models such as random forest, gradient boosting decision tree, light gradient boosting machine, logistic regression, support vector machine, and multi-layer perceptron were considered to evaluate the performance of tires in compliance with production standards. Statistical methodologies had to be adopted to deal with the small number of samples training the machine learning models. The random forest and logistic regression models achieved the best performances with an accuracy of 92% and 96%, respectively. The Local Interpretable Model-Agnostic Explanations and SHapley Additive exPlanations methods were used to identify the relevant process variables. The approach presents the global and local interpretations of the results, allowing for a deeper understanding of how process variables discriminate the fault. The proposed framework determines reference values for each selected process variable for tire manufacturing in compliance with quality standards. This approach provides a relevant tool for quality control management in tire manufacturing industries.

Population genomics of the invasive Northern Giant Hornet Vespa mandarinia in North America and across its native range

The northern giant hornet Vespa mandarinia (NGH) is a voracious predator of other insect species, including honey bees. NGH’s native range spans subtropical and temperate regions across much of east and southeast Asia and, in 2019, exotic populations of the species were discovered in North America. Despite this broad range and invasive potential, investigation of the population genomic structure of NGH across its native and introduced ranges has thus far been limited to a small number of mitochondrial samples. Here, we present analyses of genomic data from NGH individuals collected across the species’ native range and from exotic individuals collected in North America. We provide the first survey of whole-genome population variation for any hornet species, covering this species’ native and invasive ranges, and in doing so confirm likely origins in Japan and South Korea for the two introductions. We additionally show that, while this introduced population exhibited strongly elevated levels of inbreeding, these signatures of inbreeding are also present in some long-standing native populations, which may indicate that inbreeding depression alone is insufficient to prevent the persistence of NGH populations. As well as highlighting the importance of ongoing monitoring and eradication efforts to limit the spread of this species outside of its natural range, our data will serve as a foundational database for future genomic studies into introduced hornet populations.

Fault diagnosis of drone motors driven by current signal data with few samples

Multi rotor unmanned aerial vehicles (UAVs) are extensively utilized across various domains, and the motor constitutes a pivotal element in the UAV power system. The majority of UAV failures and crashes stem from motor malfunctions, underscoring the imperative need for comprehensive research on fault diagnosis in UAV motors to ensure the stable and reliable execution of flight tasks. This study focuses on quadrotor UAVs as the research subject and devises targeted fault simulation experiments based on the structural features and operational characteristics of the DC brushless motor used in quadrotor UAVs, specifically examining the stator, rotor, and bearings. To address challenges related to the UAV’s own loads, limited space for redundant parts, and the high cost and difficulty associated with installing sensors for traditional fault diagnostic signals such as vibration and temperature, this study opts to use current signals as a substitute. This approach resolves the issue of challenging data collection for UAVs and investigates a current signal based fault diagnosis method for UAV motors. Lastly, in response to the limited training samples available for fault data due to the UAV’s highly sensitive characteristics regarding the health status of its components and flight stability, traditional machine learning and deep learning methods encounter difficulties in identifying representative features with a small number of training samples, leading to the risk of overfitting and reduced model accuracy in fault diagnosis. To overcome this challenge, we propose a hybrid neural network fault diagnosis model that incorporates a width learning system and a convolutional neural network (CNN). The width learning system eliminates temporal characteristics from the original current signal, capturing more comprehensive and representative sample features in the width feature space. Subsequently, the CNN is employed for feature extraction and classification tasks. In empirical small sample fault diagnosis experiments using current signal data for UAV motors, our proposed model outperforms other models used for comparison.

Transient Stability Assessment of Power Systems Based on CLV-GAN and I-ECOC

In order to improve the multi-class assessment performance of transient stability in power systems, a multi-class assessment model that combines the CLV-GAN algorithm with an improved error-correcting output coding technique is proposed in the paper. To address the issue of the small number of unstable samples in power systems, a sample generation model is constructed by combining a dual-encoder VAE with a GAN network. The model generates effective artificial samples to balance the sample ratio between categories by learning the latent distribution of aperiodic and oscillatory unstable samples from the distribution. The decomposition method based on an improved error-correcting output coding algorithm is applied to convert the multi-class problem into a decision fusion issue for binary models. This method improves the overall performance of the multi-class model, particularly significantly increasing the recognition accuracy of discrimination against oscillatory unstable samples and reducing the safety hazards in the operation of power systems. The simulation validation was conducted on the IEEE 39-bus and IEEE 140-bus systems to confirm the effectiveness of the proposed model.

Flow-through imaging and automated analysis of oil-exposed early stage Atlantic cod (Gadus morhua)

Toxicology studies in early fish life stages serve an important function in measuring the impact of potentially harmful substances, such as crude oil, on marine life. Morphometric analysis of larvae can reveal the effects of such substances in retarding growth and development. These studies are labor intensive and time consuming, typically resulting in only a small number of samples being considered. An automated system for imaging and measurement of experimental animals, using flow-through imaging and an artificial neural network to allow faster sampling of more individuals, has been described previously and used in toxicity experiments. This study compares the performance of the automated imaging and analysis system with traditional microscopy techniques in measuring biologically relevant endpoints using two oil treatments as positive controls. We demonstrate that while the automated system typically underestimates morphometric measurements relative to analysis of manual microscopy images, it shows similar statistical results to the manual method when comparing treatments across most endpoints. It allows for many more individual specimens to be sampled in a shorter time period, reducing labor requirements and improving statistical power in such studies, and is noninvasive allowing for repeated sampling of the same population.

Touch to Learn: A Review of Haptic Technology's Impact on Skill Development and Enhancing Learning Abilities for Children

Early childhood education is critical in shaping children's intellectual and motor skills as it provides a solid foundation for cognitive, social, and emotional development, which highly depends on spatial thinking. Haptic feedback can be effectively used for educational and training purposes, particularly in fields such as physics, math, and arts, offering a more interactive learning media and supporting kinesthetic learners by its nature. Herein, different ways of implementing haptic feedback on different educational scenarios from the perspective of technological development and their impact on children's learned skills and outcomes (e.g., their motivation, their analytical or spatial thinking abilities, or fine motor skills) will be examined. This article provides an overview of how haptic feedback has been implemented in different learning scenarios for children. Particularly, it is indicated that haptics can potentially improve early childhood learning outcomes and spatial reasoning skills as it can increase children's interest, participation, performance in educational activities, and analytical ability. The major drawbacks of the current studies, such as variance in participants’ learning challenges and small sample numbers are also highlighted.

High Resolution Ranging with Small Sample Number under Low SNR Utilizing RIP-OMCS Strategy and AHRC l1 Minimization for Laser Radar

This manuscript presents a novel scheme to achieve high-resolution laser-radar ranging with a small sample number under low signal-to-noise ratio (SNR) conditions. To reduce the sample number, the Restricted Isometry Property-based optimal multi-channel coprime-sampling (RIP-OMCS) strategy is established. In the RIP-OMCS strategy, the data collected across multiple channels with very low coprime-sampling rates can record accurate range information on each target. Further, the asynchronous problem caused by channel sampling-time errors is considered. The sampling-time errors are estimated using the cross-correlation function. After canceling the asynchronous problem, the data collected by multiple channels are then merged into non-uniform sampled signals. Using data combination, target-range estimation is converted into an optimization problem of sparse representation consisting of a non-uniform Fourier dictionary. This optimization problem is solved using adaptive hybrid re-weighted constraint (AHRC) l1 minimization. Two constraints are formed from statistical attributes of the targets and clutter. Moreover, as the detailed characteristics of the target, clutter, and noise are unknown before the solution, the two constraints can be adaptively modified, which guarantees that l1 minimization obtains the high-resolution range profile and accurate distance of all targets under a low SNR. Our experiments confirmed the effectiveness of the proposed method.