Miracle-dz/UniFAR-FTUs huggingface dataset:
Chinese Medical Word Embedding Based on Transfer Learning

Word embedding has been successfully applied to Natural Language Processing (NLP) tasks. In this paper, we learn Chinese medical word embeddings in different ways and use the same corpus to evaluate the quality of the word embeddings. Firstly, we perform word embedding training through target domain data combined with data from different domains to achieve word embedding sharing and enrich the semantic information of word embeddings; Secondly, static word embedding and dynamic word embedding are combined to solve semantic ambiguity by adding dynamic word embedding. Finally, word embedding sharing combined with parameter migration is used to reduce the use of training data and adjust word embedding. Furthermore, we evaluate the quality of word embeddings obtained by different models through three metrics: word analogy, word embedding visualization, and named entity recognition task. By comparing the quality of word embedding in different models, we found that our proposed method improved in each evaluation result.
Variational Adversarial Active Learning Assisted Process Soft Sensor Method

Soft sensor methods have been widely applied in process industries to predict key quality variables that cannot be measured online. However, labeled samples to construct models are often limited because quality variables are difficult to be obtained. Additionally, due to the instrument of redundant sensors, the process data is high-dimensional with strong correlations. In this paper, an active learning soft sensor framework named variational adversarial active learning (VAAL) is developed to select informative unlabeled samples to enhance prediction performance. The sampling strategy of VAAL learns a latent space using a variational autoencoder (VAE) and an adversarial network trained in a way of minimax game. The VAE tries to trick the adversarial network into predicting that all samples are from the labeled pool, while the adversarial network learns how to discriminate between dissimilarities in the latent space. The Gaussian process regression model is adopted in VAAL as a base soft sensor. The prediction results of an industrial debutanizer column demonstrate the advantages of VAAL as compared to the existing active learning strategies.
An Approach to Multivariate Covariate-Dependent Quantile Contours With Application to Bivariate Conditional Growth Charts

Multivariate quantile contours are useful in numerous applications and have been studied in different contexts. However, no easy solutions exist when dynamic and conditional quantile contours are needed without strong distributional assumptions. In this article we propose a new form of bivariate quantile contours and a two-stage estimation procedure to take time effect into account. The proposed procedure relies on quantile regression for longitudinal data and is flexible to include potentially important covariates as necessary. In addition, we propose a visual model assessment tool and discuss a practical guideline for model selection. The performance of the proposed methodology is demonstrated by a simulation study, as well as an application to joint height–weight screening of young children in the United States. We construct bivariate growth charts by a nested sequence of age-dependent and covariate-varying quantile contours of height and weight, and use it to locate an individual subject's percentile rank with respect to a reference population. Our work shows that the proposed method is valuable for pediatric growth monitoring and provides more informative readings than the conventional approach based on univariate growth charts.
A Price-Based Strategy to Coordinate Electric Springs for Demand Side Management in Microgrids

Electric springs (ESs) have proven effective for integrating renewable generation into power systems. An ES connected in series with a non-critical load forms a smart load whose consumption can be dynamically controlled for voltage regulation and demand side management. In most existing applications, smart loads have been devoted to providing services to the grid without accounting for their own interests. The novelty of this paper is to propose a price-based strategy to coordinate the operation of multiple ESs in microgrids. Smart loads consisting of ESs connected to electric water heaters are modeled as rational agents that locally optimize their own objectives by adjusting their consumption schedules in response to price/control signals. Such signals are determined at the microgrid central controller (MGCC) when solving the microgrid operation scheduling problem formulated to minimize the microgrid operation cost taking into account the smart loads’ consumption schedules. An iterative optimization algorithm determines the equilibrium between the microgrid and smart loads’ objectives requiring only the exchange of price/control signals and power schedules between the local controllers and the MGCC. Case studies show the effectiveness of the proposed strategy to economically benefit both the microgrid and smart loads when scheduling their operation.
Bringing Computational Thinking Skills Into Physics Classroom Through Project-Based Learning

Computational thinking is a fundamental thinking skill for students in the 21st-century learning era. This study aims to integrate computational thinking skills in physics through project-based learning. Data were gathered through observation, interviews, and portfolio documents. The results indicated three main categories of computational thinking: decomposition, abstraction, and simulation. The implementation of project-based learning can develop students’ computational thinking skills. Students are trained to solve physics problems analytically and precisely in structured and logical thinking through a project. This research is expected to be the information for physics teachers to integrate computational thinking in the classroom as a 21st-century learning skill.
Optimized Deep Learning technique for Brain Tumor Segmenting, Contouring, and Detection

The most vital task in brain tumor detection systems is to isolate the abnormal tissues from healthy brain tissues. Considering the cancer diagnosis of brain tumors, the manual segmentation from the vast stack of images obtained through MRI is complicated and time-consuming work. In recent years, the development of various approaches, including computer-aided techniques for segmenting brain tumors, has become more popular in several clinical applications. The objective is to provide an inclusive synopsis for brain-tumor segmentation methods of MRI acquired images. This paper involves Segmenting tumors with greater efficiency and categorization of brain tumor with Deep Q-network, thus, helping the doctor in deriving anatomy and pathology regarding the classification of tumor types.
Statistical Filtering Evaluation for Improved 3D Point Cloud Image Structure

In this paper the concept of statistical filtering is implemented for point cloud image analysis and 3D usergenerated data. The entire implementation is carried out using free software and the programming language C++ on self-generated examples of works of art from Ervin Wurm’s exhibition “One minute forever” held in Belgrade in 2022. The evaluation results point out the importance of statistical filtering and the improvement of quality that it brings, both from the objective and visual aspect, as well as from the aspect of the point cloud image structure itself.
Image Sharpening by Selecting Number of Echoes in ISAR Imaging

An algorithm is developed to sharpen the image by selecting the number of echoes in ISAR imaging. Entropy is used to measure the sharpness of the image: smaller entropy means larger sharpness. The number of echoes is selected to minimize the entropy of the image. In the optimization, the azimuth discrete Fourier transform of each round is computed from the azimuth discrete Fourier transform of last round. This improves the computational efficiency significantly. The results of field data indicate the effectiveness of this algorithm.
Spatial Feature Extraction using Pretrained Convolutional Neural network for Hyperspectral Image Classification

Hyperspectral images (HSIs) captured a detail range of electromagnetic spectrum from visible to near to infrared to each pixel. Due to variability of spectral data and lack of labeled data HSI classification is a challenging work. The convolutional neural network (CNN) have successfully used in object detection and classification. A model based on spatial features extracted using pretrained convolutional neural network presented in this paper. The features are extracted at fully connected layer. Our proposed model consists of principle component analysis (PCA) for dimension reduction, followed by pretrained CNN for the purpose of spatial features and SVM classifier. In experiments, we used pretrained CNN (AlexNet) and HSI data set (Indian Pine). Experimental result with HSI dataset demonstrate that classifier based on our proposed model provide very competitive performance of overall accuracy (97.76%), average accuracy (98.77%) and K-score (0.9732). In addition, results of the experiments are compared with state-of-the-art HSI classification methods.
A Novel DC-link Voltage Observer with Time Delay Compensation for Small DC-link Capacitor-based IPMSM with EEMF Based Sensorless Control

DC-link voltage sensor is very important for permanent magnet synchronous machines (PMSMs) control system, especially for small dc-link capacitors-based PMSMs control system where dc-link voltage is always fluctuating. However, the voltage sensor is expensive and may suffer measurement errors and failures in the harsh environment. Therefore, this paper proposes a quasi-proportional integral resonant type of voltage observer to estimate the dc-link voltage accurately based on inverter power balance. The proposed voltage observer could lower the cost and increasing the system reliability. In addition, the estimation delay in the proposed observer due to low pass filter (LPF) will cause the large voltage observation error since the dc-link voltage is fluctuating. To eliminate the effect of estimation delay on the system, this paper predicts and reconstructs the estimated voltage based on the periodicity of fluctuating dc-link voltage. The accuracy of the dc-link voltage observer and the efficiency of the delay compensation strategy have been demonstrated by the experiments.
Spatial-temporal Pattern Recognition for Data Identification and Tagging Based on Power Curve in Wind Turbines

Due to variational environmental conditions and varied adaptive control strategies, the operation states of wind turbines are continuously changing, leading to diverse types of samples in the power curve. Different kinds of samples may contain noises or valuable information for specific downstream tasks and thus need to be correctly identified and labeled. To this end, this paper proposes a spatial-temporal pattern recognition algorithm for data identification and tagging. According to spatial distribution and temporal characteristics, all data points are divided into four groups including normal samples, isolated outliers, change points, and faulty samples. Then, some distances based on the dynamic time warping method are defined to make evaluations and then serve as indicators for achieving precise tagging of each category. Case studies and comparative experiments are conducted to verify the effectiveness and superiority of the proposed method.
Deep Cost-sensitive Learning for Wheat Frost Detection

Frost damage is one of the main factors leading to wheat yield reduction. Therefore, the detection of wheat frost accurately and efficiently is beneficial for growers to take corresponding measures in time to reduce economic loss. To detect the wheat frost, in this paper we create a hyperspectral wheat frost data set by collecting the data characterized by temperature, wheat yield, and hyperspectral information provided by the handheld hyperspectral spectrometer. However, due to the imbalance of data, that is, the number of healthy samples is much higher than the number of frost damage samples, a deep learning algorithm tends to predict biasedly towards the healthy samples resulting in model overfitting of the healthy samples. Therefore, we propose a method based on deep cost-sensitive learning, which uses a one-dimensional convolutional neural network as the basic framework and incorporates cost-sensitive learning with fixed factors and adjustment factors into the loss function to train the network. Meanwhile, the accuracy and F1 score are used as evaluation metrics. Experimental results show that the detection accuracy and the F1 score reached 0.943 and 0.623 respectively, this demonstration shows that this method not only ensures the overall accuracy but also effectively improves the detection rate of frost samples.
Image-based Obstacle Avoidance using 3DConv Network for Rocky Environment

Autonomous navigation systems are an essential part of Unmanned Ground Vehicles (UGVs) since they allow for navigating without supervision in conditions where communication is not available or the existence of high delays which prevent direct communication. One fundamental part of autonomous navigation is obstacle avoidance. Typical approaches utilize some form of distance measuring-based sensors like LIDAR or SONAR. However, such devices have a relatively higher cost in comparison to conventional RGB cameras in addition to introducing complexity in data processing which results in an increase in computational cost and power consumption. In this work, we use sequential RGB data and a Conv3d-based network to create a real-time obstacle avoidance system with high accuracy, low latency, and low processing cost. For training, we used Unreal Engine 4 based simulator to collect a dataset to train the network. Testing the system in a simulated environment using the same simulator showed the ability of the network to avoid obstacles in a realistic environment where rocks of different sizes and shapes were used. Future work can include improving in terms of performance and processing time as well as implementing the network with a real word working prototype and comparing the simulated results with actual performance.
Feature Extraction and Classification of Objects Using ML Model for FMCW Radar Signal

Detection and Classification of multiclass objects like moving humans, and moving vehicles plays a major role in Border surveillance to detect the threats caused by our enemies. The use of millimeter wave radar sensors in multiclass classification algorithms is of great significance since the sensor maintains to be robust in severe weather like fog, snow, etc. In this paper, Frequency modulated continuous wave (FMCW) radar operating at 60-64 GHz is used as the sensor to detect and classify objects. The main objective of the paper is to extract the features from the point cloud information and classify the object using the Bagged ensemble model. To verify the validity and robustness of the proposed feature vector, two datasets have been used, one for moving humans and another for moving vehicles. The dataset is taken for humans and vehicles at different distances, velocities, and angles. Experimental results show that the proposed ML algorithm achieves higher classification accuracy than a conventional method based on signal features like velocity and signal strength.
The IEM2M rough-surface scattering model for complex-permittivity scattering media

The integral equation model (IEM) was developed in the late 1980s and arguably became the most cited and implemented rough-surface scattering model in the field of radar remote sensing for Earth observation. It was derived by applying a second-order iteration in the incident electromagnetic field to the integral equations of the surface fields as given by Poggio and Miller. It is thus an extension of the first-order, Born approximation of these equations that produce the classical Kirchhoff approximation. The IEM has been subject to numerous amendments and variations over the last 20 years due to the imperfect introduction and handling of the Weyl representation of the spherical wave in its first version. The work presented here is a further development of the contribution made by the same author in 2001 (IEM2M), which was the first version of IEM able to blend analytically both the Kirchhoff and the small-perturbation approximations for the bistatic case. The improvement reported in this article is concerned with the inclusion of evanescent waves in the formulation of the model and the extension of the range of applicability of the second-order scattering terms to interfaces with complex-permittivity scattering media.
Deep Learning Features Restoration and Regional Longitudinal Fitting of Computed Tomography Images using Convolution Neural Network

Positron Emission Tomography (PET) is suggested for its high potential Deep Learning (DL) diagnostic imaging with a profound learning approach. The network training is done using clear images but reconstructing the low resolution images using Poisson operation. In training the Convolutional Neural Networks (CNN) at a default noise level, a major issue for their generic applicability is the noise level discrepancy. The noise level varies considerably in each iteration reduces the overall efficiency. The results and measured efficiency loss in different noise environments with various noise levels due to inadequate current trials is also presented. To fix this problem, a local linear fitting function is represented before improving the image quality. It indicates that the resulting approach is resilient to noise levels despite the network being educated at a fixed noise level. The proposed protocol is demonstrated to exceed traditional approaches based on total variance and penalty by mean and standard deviation via simulations and trials.
Spectral Feature Recognition Model based on Waveform Matching Algorithm

The algorithm first decomposes the original waveform into three layers, and uses the improved absolute value difference method, the improved sequence similarity method, and the normalized cross-correlation method for matching in each layer, and finally achieves accurate matching. Fantasy Skyline brings together his life’s creative approach and presents it with great maturity, with a bright and smooth melody in its structure. The entire musical composition contains more than 40 poems and is named after 14 of them. The creation of the works not only contains the passion and fantasy in the verse, but also is full of many profound irony. The search strategy of the hill-climbing method and the particle swarm optimization algorithm of the swarm intelligence algorithm are respectively used to realize the non-traversal search of the waveform matching algorithm.
AI Buildings:Design of Artificially Intelligent Buildings in the Energy Sector with an Autonomous Federated Learning Approach

In Recent Years Intelligent buildings are a part of Smart Environments, with the ability to adapt their behavior on their own to meet the requirements of the occupants. Intelligent buildings require AI to be included in their systems so that they can reason and make decisions on the fly. In order for a structure to act like an intelligent one, it must monitor its energy use and have an automatic on/off system for all of its lights, fans, doors, windows, and other electrical components. Reducing a building’s energy use in a cognitively enabled manner requires first collecting and analyzing data on the building’s power consumption over a significant sample period of time. Cluster the data into distinct groups according to the time period, power consumption, and user behavior using support vector machines and logic regression approaches. Insights gained from such patterns will be invaluable in determining where to focus efforts to reduce energy consumption. It guarantees that our observed outcomes are suitable for a one-year predictive study based on the data sample worked with the data. Moreover, compared our results to those of other machine learning algorithms and were pleased with the overall performance of our approach.
RSSI Indoor Localization Method Base on RAUKF

Location-based services(LBS) are widely applied in public safety, smart-hospital, industry and other scenarios recently. Demands of customer side presents many challenges for indoor positioning technology, such as accuracy, cost, power consumption, etc. Limited by the complexity of the indoor environment, the accuracy of the current applied in-door positioning system is relatively limited. Kalman filtering is a widely applied optimal estimation algorithm for linear systems, but it performs poorly for nonlinear systems. In the practical application of indoor positioning of kalman filter, the parameters affected by indoor environmental factors also restrict the positioning accuracy. In this paper we proposed an RSSI ranging-based indoor positioning algorithm, which fits the positioning results through the robust adaptive unscented kalman filter(RAUKF) to improve the positioning accuracy. An Singular Value Decomposition(SVD) method is deployed in the UKF to improve the numerical stability. The Root Main Square Error(RMSE) of 0.65m was achieved in MATLAB simulation experiment with four anchor nodes deployed.
Towards an Improved Implementation of Hardware Transactional Memory on Asymmetric Processors

The paper describes typical challenges in a computer architecture research demonstrated on a case study of hardware transactional memory. It shows how the proposed concepts and solutions are implemented in a software simulator. Then, various experiments are carefully prepared in order to evaluate the performance of the proposed transactional memory implementation. The transactional memory in our experiments was paired with an asymmetric multicore processor with a support for transaction migration. We present design decisions how to implement the transactional memory, the transaction migration and the different cache memory subsystem organization in the simulator. Also, we varied cache memory subsystem organization and parameters in the experiments. Important issue was also how to organize data collected from the experiments, and how to analyze and visually present them. Finally, the paper demonstrates the use of a benchmark suite for the transactional memory. Problems that we encountered during research are pointed out and discussed and solutions for them are provided. The paper concludes with brief lessons we have learned in this research effort.