Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-objective discrete salp swarm optimizer for solving multiple-product U-shaped disassembly line balancing problem

The disassembly line balancing problem is one of the most significant methods in handling large quantities of waste electrical and electronic equipment efficiently problems. In this paper, we propose a U-shaped layout disassembly line and set up a mathematical model to maximize the disassembly profit and minimize the disassembly time of hazardous parts. Therefore, an improved Multi-objective Salp swarm algorithm based on Pareto which combines with a stochastic simulation method is proposed to solve such a U-shaped disassembly line balancing problem. Some cases are used to verify the effectiveness of the proposed method. The results show the proposed method can solve the U-shaped disassembly line balancing problem and outdo the other two optimization algorithms: Nondominated Sorting Genetic Algorithm-II (NSGA-II) and Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) in the quality of solutions.
Multi-source image target detection technology based on salient feature fusion

The ground target can be easily affected by the complexity of ground objects and photoelectric interference, so the accuracy of target detection in complex background is not always satisfying. To this end, joint target detection of Infrared/Visible image, IR/VI, is adopted, and the feature representation is achieved by applying the method of deep image feature extraction. According to the different imaging characteristics of the target, the feature fusion is carried out to form a unified target feature representation. As a result, target detection based on deep learning can be achieved by the model established by such fused feature. Experiments show that the proposed method can effectively improve the accuracy of target detection.
Disruption prediction on EAST tokamak using a deep learning algorithm

In this study, a long short-term memory (LSTM) model is trained on a large disruption warning database to predict the disruption on EAST tokomak. To compare the performance of the proposed model with the previously reported full convolutional neural network (CNN) (Guo et al 2020 Plasma Phys. Control. Fusion 63 025008), the same data set and diagnostic signals are used. Based on the test set, the area under the receiver operating characteristic curve, i.e. the AUC value of the LSTM model is obtained as 0.87, and the true positive rate (TPR) is sim87.5%, while the false positive rate (FPR) is sim15.1%. Since the LSTM model is more sensitive to radiation fluctuations than CNN, the prediction performance of LSTM model is inferior to that of CNN model (for CNN, AUC sim 0.92, TPR sim 87.5%, FPR sim 6.1%). However, the advance warning time of LSTM model is 14 ms earlier than that of CNN. To reduce the FPR and improve the performance of the model, more fast bolometer channels are added as the input signals of the LSTM model, including the radiation from the upper and lower edges and the plasma core. Consequently, for the same test set, the AUC value increases to 0.89, and the FPR decreases to sim9.4%, but the TPR also decreases to sim83.9%. In addition, the sensitivity of the model to radiation fluctuations caused by impurity behavior decreases significantly, and the warning time becomes 8.7 ms earlier as compared to that of the original model. Overall, it is proved that deep learning algorithms exhibit immense application potential in the disruption prediction of long-pulse fusion devices.
Fluid-structure interaction Simulation for Hydro-elastic Performance of marine Propeller at Full-Scale

In this study, CFD-FEM bi-directional fluid structure interaction (FSI) methodology using STAR-CCM+ is adapted for the hydro-elastic interaction simulation of the propeller at full-scale. To validate the FSI simulation reliabilities, the open-water curve of the rigid and NAB propellers VP1304 at model scale are computed. Then, the propeller KP505 is employed for the open-water test to study scale effect and hydro-elastic performance. The change of thrust coefficient, torque coefficient, efficiency, stress distribution, deformation are presented. From the results, the hydrodynamic difference due to scale effect and hydro-elastic performance are observed. Analysis of structural response of NAB propeller shows that scale effect plays a role.
Enhancing multi-functionality of reconfigurable transistors by implementing high retention capacitorless dynamic memory

A key indicator of multi-functional attributes of a transistor is technological competiveness vis-à-vis existing architectures. Apart from the well-known logic circuit implementation through reconfigurable field effect transistors (RFETs), this work showcases feasible memory operation by realising capacitorless (1T) dynamic random access memory (DRAM). The memory operation in RFET is achieved through back control gate which creates an electrostatic potential well to store holes. Due to the inherent features of RFET architecture a wider and deeper potential well results in a significantly high retention time (RT) of 2.3 s at 85 °C for a total length of 90 nm. Apart from high retention, RFET based 1T-DRAM exhibits a low write time of ∼2 ns, sense margin (SM) of ∼76 µA µm−1 and a high current ratio (CR) of ∼105. Benchmarking the performance metrics against previously published results indicates competitiveness for RT in terms of total length, storage volume and high temperature operation. Critical insights aiding competitive multi-functional behaviour through 1T-DRAM highlights the possible implementation of logic and memory blocks with RFETs.
Embodiment design of Moringa Oleifera rotary dryer using VDI 2221 method

In order to find the solution to the current problems of drying process of Moringa Oleifera in East Nusa Tenggara (NTT)-Indonesia such as requiring a large enough space and power, expensive, non-ergonomic and need to turn the leaves over at least once manually using hand during the drying process that can contaminate the process, a new suitable drying method is necessary. Therefore, embodiment design of drying method using VDI 2221 method was studied. The results indicated that a new suitable drying method of moringa oliefera using VDI 2221 method can be made by successfully designing and manufacturing a rotary dryer based on the selection of the best concept from a variety of existing design concepts. In summary from the perspective of engineering design, after testing, the rotary dryer was suitable and can be used to solve current problems of drying process of Moringa Oleifera in East Nusa Tenggara-Indonesia.
Research on Algorithm of fighter landing gear in bad video image

The retracted and retracted state of landing gear is a problem that needs to be paid close attention to in the landing process of fighter. The traditional visual observation is easy to be affected by the climate environment. Aiming at the problems of small target, low definition and difficult detection of fighter landing gear in bad video images, a YOLOv4 target detection algorithm based on image enhancement is proposed to detect fighter landing gear. In order to improve the accuracy of landing gear detection, ACE algorithm is first applied to the video image to enhance the video image, then the main feature extraction network of YOLOv4 is used to extract the feature information, and K-means clustering algorithm is used to cluster and analyze the number and aspect ratio dimension of target candidate frames. Experiments on the dataset show that the detection accuracy after image enhancement reaches 88.12%, which is 1.19% higher than the original image; The recall rate reached 73.44%, an increase of 4.17%; The value of mAP reached 82.65%, an increase of 1.08%.
Changes in Pork Freshness Indicators When Stored at Positive Temperatures

The article examines the processes occurring during the ripening of pork meat during storage at a temperature of 0–4 °C. Particular attention is paid to a group of chemical indicators of freshness of raw meat: acidity (pH), mass fraction of sodium chloride, and the presence of protein breakdown products. The author concludes that instrumental methods, along with methods of organoleptic control of raw meat, make it possible to assess the properties of meat more accurately. Based on the study of samples, it was established that measuring the pH (acidity) of meat 24 hours after slaughter and in the subsequent period allows characterizing in detail the autolysis process and detect signs of NOR, PSE or DED. Considering changes in pH values, it is possible to determine the direction of use of raw meat. It was revealed that the mass fraction of sodium chloride does not change during maturation and storage under the conditions under study. In all samples, the presence of protein degradation products was not detected.
Analysis on reducing eddy current loss of axial motor by permanent magnet segmentation

This paper gives the causes of eddy current loss of permanent magnet in axial permanent magnet motor, and puts forward the method of reducing eddy current loss by circumferential and radial combined sectional insulation of permanent magnet. The principle analysis and analytical formula derivation of permanent magnet sectional insulation to reduce eddy current loss are carried out. Finally, Maxwell three-dimensional finite element simulation is used to verify that the circumferential and radial combined sectional insulation of permanent magnet can greatly and quickly reduce the eddy current loss of permanent magnet of axial motor.
A Modified Online Intelligent Method to Calibrate Radar and Camera Sensors for Data Fusing

This paper introduces a modified online intelligent calibration method to calibrate radar and camera sensors in traffic scenarios. Radar and camera sensor calibration is to construct a one-to-one mapping between the radar coordinate system and the image pixel coordinate system. Based on the currently widely used artificial intelligence method (deep neural networks), camera can accurately recognize the target type. For the research and application of radar sensor and camera sensor fusion, the most important thing is to synchronize the sensor sampling time of the radar and the camera and space calibration. This paper emphasizes on spatial calibration. At present, the common calibration methods are the calibration method based on the internal and external parameters of the camera, the four-point calibration method, and the neural network calibration method. However, these three calibration methods have significant advantages and disadvantages. In the field of transportation, the disadvantages are even more prominent. This paper proposes a modified online intelligent calibration method for the application field of radar and camera sensors in traffic scenarios. We have proved through simulation experiments that this method is less time-consuming and manual operation while ensuring high-precision calibration performance.
Research and application of small target detection method

With the advent of the big data era, more and more data are used in image detection. In order to solve the problem of small target detection in traditional image detection algorithms, this paper proposes an improved target detection algorithm based on YOLO-v5. The algorithm proposes to connect two models in series, using the former model for target pre-positioning, and the latter model for target detection. This method trains two different model parameters, tests them separately, and finally connects them for target detection. The test results show that the improved algorithm improves the ability in small target detection, and at the same time has a speed advantage.
Robust Unsupervised Feature Selection Based on Elastic Distance Loss

In recent years, it is a desirable way to introduce graph learning into feature selection, and it has achieved good performance in feature selection tasks. But three issues need to be concerned: (1) how to learn the similarity of samples during graph construction adaptively; (2) the performance of feature selection heavily depending on the construction of the graph; (3) most of the feature selection algorithms all use L2,1-norm for feature selection, which is not suitable for all data distributions. In this paper, we propose a robust feature selection framework to solve the above problems, which incorporated data reconstruction, graph embedding, and feature selection into a uniform framework. We use the probability of the neighbors to reconstruct the original data adaptively. Then, a novel adaptive loss function, namely, the elastic distance loss function is proposed. Finally, we compared seven state-of-art unsupervised feature selection algorithms in several benchmark datasets, and the experimental results proved the effectiveness of our proposed model.
Comparative analysis of 2D-PCA based dimensionality reduction and feature extraction

The complex spectral and spatial characteristics of hyperspectral remote sensing images (HSI) lead to higher time-consuming in classification task. To address this question, we introduced the 2D-PCA dimensionality reduction method of linear mapping in the two-dimensional spatial domain on the basis of linear dimensionality reduction in the spectral domain, thereby compressing the complex spatial structure information of HSI into a limited low-dimensional space, and realizing space-spectrum dimensionality reduction and information fusion. The experimental results on three classic data sets of Salinas, Tea Farm, and Indian Pines show that 2D-PCA has a strong ability to condense and compress spatial structure characteristics. Compared with popular deep learning frameworks such as CNN and Mixer-MLP, conventional machine learning models based on 2D-PCA have significant advantages in terms of computing time under the premise of controllable accuracy loss, which makes 2D-PCA a promising method for dimensionality reduction and feature expression in hyperspectral pixel-wise classification.
Reliable Single-image Denoising for Adaptive Optics Scanning Laser Ophthalmoscopy

A reliable single-image denoising method is presented for adaptive optics scanning laser ophthalmoscopy. This method firstly averaged multiple images and then used the averaged image as the reference to adjust the parameters of the filtering process that was subsequently applied to other individual images. Six filtering methods, including the mean, median, Gaussian, fast adaptive nonlocal synthetic aperture radar despeckling, K-single value decomposition, and block matching and three-dimensional filtering, were utilized. The effectiveness of our method was verified based on the comparison of sets of images without and with parameter adjustments. Furthermore, we applied the same parameter settings as those obtained from the filter adjustments of another adaptive optics scanning laser ophthalmoscope image acquired by the same instrument. The filtered images showed that the parameter-adjusted filters work well on other images, which is helpful for improving the image quality of adaptive optics scanning laser ophthalmoscope images.
Ensuring the Environmental Safety of Large Cities Based on an Innovative Approach to Predicting Atmospheric Air Pollution

Sustainable development of territories should be based on ensuring sufficient quality of the air basin. Air quality management, in turn, is based on adequate predictive models that allow the development of a number of measures to improve its quality. In this regard, the article is devoted to the discussion of issues of ensuring the environmental safety of territories, including rural ones, on the basis of the innovative approach developed by the authors to the implementation of the forecast of the air quality of the lower layer of the atmosphere. The choice of the object of study–the city of St. Petersburg–is connected with the fact that the city has a network of posts for environmental monitoring of the state of the air environment, providing up-to-date information for making a forecast scheme. As a result of the conducted research, the authors identified groups of meteorological and synoptic characteristics, as well as formed databases of hydrometeorological data and series of concentrations of a number of impurities for the period from 2006 to 2014. It is assumed that the successful application of the developed scheme for predicting the quality of atmospheric air will ensure the environmental safety of the population of urban and rural areas.
Research on Multi-objective Trajectory Prediction Algorithm Based on Driving Intent Classification

The trajectory prediction of the multi-object vehicle ahead plays a huge role in improving the vehicle’s driving safety and improving the planning and traffic efficiency of the vehicle. However, due to the uncertainty of the driving intention of the multi-object vehicle and the uncertainty of the vehicle dynamics, its trajectory prediction faces a huge challenge. First, the fuzzy C-means (FCM) method is used for multi-object trajectories. After offline training is carried out using the relevant information of the vehicle trajectory, the driving intention can be automatically fuzzy classification. Secondly, the long short-term memory (LSTM) method uses the history and current trajectory information of multi-object vehicles ahead to predict the future trajectory under different driving intentions. Then, according to the fuzzy classification results of the driving intention, the predicted trajectories are merged, and the trajectory prediction of the multi-objective vehicle ahead in the next 1s is realized through an iterative method. Finally, use real vehicle data for experimental verification. The results show that 92.1% of the lateral distance prediction error in the tenth step is less than 0.13m. The maximum distance prediction error is 0.54m. In the longitudinal distance, the prediction error of 94.5% is less than 0.6m. The maximum distance prediction error is 1.0m.
Research on Unbalanced Sample Segmentation of Remote Sensing Image

This article proposes a framework for the unbalanced sample segmentation of remote sensing images on an open data set. For the few sample categories, improved by converting it into binary segmentation and combining with weighted cross-entropy loss, and then merged with the segmentation result of the sufficient sample categories. Finally, the mIoU (mean Intersection of Union) of the 8 categories is increased by 4.5% compared to the results before the improvement, especially, the results of the few sample category road and grassland are increased by 10.2% and 9.7%. The experiments show that the framework can greatly improve the segmentation performance of the few sample categories, and have a good guiding significance for the problem of multiclass unbalanced sample segmentation.
Garbage detection and classification based on improved YOLOV4

Object detection is an important research task in computer vision. With environment problems becoming seriously, garbage detection has become a hot direction in object detection. Though great progress has been made in garbage detection, there still exists challenges for general-purpose detectors with no public datasets and low recognition accuracy rate. Based on YOLOV4 model, this paper proposes an improved YOLOV4 detector for garbage detection. Specifically, CBAM is added to the feature extraction network in order to better extract features in deep networks, and focal function is integrated into the loss function to improve class imbalance. When preparing the data, environment information and random information are added to pictures to simulate the distribution of garbage in real environment, and finally an urban household garbage dataset containing 47 classes of 45,910 images named TrashSet has been produced. Experiments results on TrashSet verify that our detector has a considerable performance, and mAP reaches 97.15%.
Research on Flower Image Classification Method Based on YOLOv5

The rapid development of deep learning has accelerated the progress of related technologies in the computer vision field and it has broad application prospects. Due to flower inter-class similarity and intra-class differences, flower image classification has essential research value. To achieve flower image classification, this paper proposes a deep learning method using the current powerful object detection algorithm YOLOv5 to achieve fine-grained image classification of flowers. Overlap and occluded objects often appear in the images of the flowers, so the DIoU_NMS algorithm is used to select the target box to enhance the detection of the blocked objects. The experimental dataset comes from the Kaggle platform, and experimental results show that the proposed model in this paper can effectively identify five types of flowers contained in the dataset, Precision reaching 0.942, Recall reaching 0.933, and mAP reaching 0.959. Compared with YOLOv3 and Faster-RCNN, this model has high recognition accuracy, real-time performance, and good robustness. The mAP of this model is 0.051 higher than the mAP of YOLOv3 and 0.102 higher than the mAP of Raster-RCNN.
Synthesis of Hexabenzylhexaazaisowurtzitane (HBIW) under Ultrasound Irradiation with Fe3O4@PCA Nanoparticles as an Efficient and Reusable Nanomagnetic Catalyst

Magnetic Fe3O4@polycitric acid (Fe3O4@PCA) nanoparticles were prepared by the reaction of Fe3O4 nanoparticles with an excess amount of citric acid at 100-160 °C. The magnetic Fe3O4@PCA nanoparticles were characterized by infrared spectroscopy (FTIR), scanning electron microscope (SEM), Transmission Electron Microscopy (TEM), Thermogravimetric Analysis (TGA) and powder X-ray diffraction (XRD). Finally, the synthesized magnetic Fe3O4@PCA nanoparticles were used as a heterogeneous solid acid catalyst for the synthesis of HBIW from benzylamine and glyoxal in acetonitrile-water solvent under ultrasonic irradiation conditions. The catalyst could be reused up to 6 times without significant loss of activity.
A Novel Text Ensemble Clustering Based on Weighted Entropy Filtering Model

Text clustering is one of the important technical bases of natural language processing, and ensemble clustering improves the robustness of text clustering. According to the existing research of scholars and experts, the quality and diversity of basic clustering have a great influence on consensus clustering, and it has a particularly significant effect on text clustering. However, there are a few pieces of research aiming at reducing the number of low-quality clustering in ensembles. This paper proposes a novel clustering filtering model based on entropy criteria. The entropy criterion is used to evaluate the uncertainty of each cluster w.r. t. the ensemble. Two indexes are proposed on the basis of the uncertainty of cluster, namely, Clustering Trend Index (CTI) which indicates the contribution of each cluster w.r. t. basic clustering, and Cluster Consistency Index(CCI) which indicates the degree of cluster dispersion in the basic clustering. The proposed clustering filtering model is built on the basis of new weight using two proposed indexes. Thereby, by dropping the low-quality clustering, the percentage of high-quality clustering will increase. A large number of experiments on various real text data sets using optimal thresholds show that the proposed method has greatly improved accuracy and robustness, and is superior to existing ensemble clustering algorithms.