Low Time Consumption Research Articles

In Vitro Culturing of Human Term Placental Explants.

The study of the human placenta has always been appealing, given the importance of this temporal organ capable of sustaining the beginning of life and development of a new human being within the womb. Culturing placental explants has been an easy and reliable method to study some placental morphological, biochemical, and physiological features for a very long time. Besides low time consumption, requirement of few resources, and wide versatility, the placental explant in vitro culture retains cell-cell interaction in a 3D structure resembling the in vivo setting, which is why it is the option of choice for many researchers in the field. This chapter will describe a simplified method for culturing explants from human term placentas.

Kalman filtering to reduce measurement noise of sample entropy: An electroencephalographic study.

In the analysis of electroencephalography (EEG), entropy can be used to quantify the rate of generation of new information. Entropy has long been known to suffer from variance that arises from its calculation. From a sensor's perspective, calculation of entropy from a period of EEG recording can be treated as physical measurement, which suffers from measurement noise. We showed the feasibility of using Kalman filtering to reduce the variance of entropy for simulated signals as well as real-world EEG recordings. In addition, we also manifested that Kalman filtering was less time-consuming than moving average, and had better performance than moving average and exponentially weighted moving average. In conclusion, we have treated entropy as a physical measure and successfully applied the conventional Kalman filtering with fixed hyperparameters. Kalman filtering is expected to be used to reduce measurement noise when continuous entropy estimation (for example anaesthesia monitoring) is essential with high accuracy and low time-consumption.

A fast, accurate and uncalibrated robotic puncture method

AbstractBackgroundRobotic puncture system (RPS) consists of an optical tracking system (OTS) and a robotic arm gripping the puncture needle. Typically, the RPS requires hand‐eye calibration before the surgery in order to obtain the relative position between the OTS and the robotic arm. However, if there is any displacement or angular deviation in either the robotic arm or the OTS, the calibration results become invalid, necessitating recalibration.MethodsWe propose an uncalibrated robotic puncture method that does not rely on the hand‐eye relationship of the RPS. By constructing angle and position graph jacobian matrices respectively, and employing Square Root Cubature Kalman Filter for online estimation. This enables obtaining control variables for the robot to perform puncture operations.ResultsIn simulation experiments, our method achieves an average error of 1.3495 mm and an average time consumption of 39.331 s.ConclusionsExperimental results indicate that our method possesses high accuracy, low time consumption, and strong robustness.

Real-time intelligent fault diagnosis of rotating machines based on Archimedes algorithm optimised Gradient Boosting

ABSTRACT Intelligent fault diagnostics is an essential part of the concept of Industry 4.0. Most of the developed techniques are usually tested by the same dataset whose data is acquired under stable conditions and not generally assessed in terms of essential metrics that set the robustness and real-time suitability of a model. Based on such drawbacks, this study aims to propose an effective fault diagnosis technique to be employable in real-world settings. For this purpose, numerous ensemble learning methods and signal processing techniques are utilized and compared to find the ultimate fault analysis model suitable for real-time settings. The best approach is optimized by employing Archimedes Optimisation Algorithm (ArchOA) to eliminate the redundant features and therefore, reduce the computational load and time consumption. It is concluded that the Gradient Boosting Decision Tree (GBDT) is the most accurate model for all fault types and the most suitable one for potential real-time analysis considering its low time consumption and model-built time. Employing ArchOA reduces time consumption by 25% by eliminating redundant features by 36%. The proposed approach successfully identified the faulty state including the fault type with an average accuracy of 99.16% for the testing set and 97.50% for the real-time analysis.

Automatic craniomaxillofacial landmarks detection in CT images of individuals with dentomaxillofacial deformities by a two-stage deep learning model

BackgroundAccurate cephalometric analysis plays a vital role in the diagnosis and subsequent surgical planning in orthognathic and orthodontics treatment. However, manual digitization of anatomical landmarks in computed tomography (CT) is subject to limitations such as low accuracy, poor repeatability and excessive time consumption. Furthermore, the detection of landmarks has more difficulties on individuals with dentomaxillofacial deformities than normal individuals. Therefore, this study aims to develop a deep learning model to automatically detect landmarks in CT images of patients with dentomaxillofacial deformities.MethodsCraniomaxillofacial (CMF) CT data of 80 patients with dentomaxillofacial deformities were collected for model development. 77 anatomical landmarks digitized by experienced CMF surgeons in each CT image were set as the ground truth. 3D UX-Net, the cutting-edge medical image segmentation network, was adopted as the backbone of model architecture. Moreover, a new region division pattern for CMF structures was designed as a training strategy to optimize the utilization of computational resources and image resolution. To evaluate the performance of this model, several experiments were conducted to make comparison between the model and manual digitization approach.ResultsThe training set and the validation set included 58 and 22 samples respectively. The developed model can accurately detect 77 landmarks on bone, soft tissue and teeth with a mean error of 1.81 ± 0.89 mm. Removal of region division before training significantly increased the error of prediction (2.34 ± 1.01 mm). In terms of manual digitization, the inter-observer and intra-observer variations were 1.27 ± 0.70 mm and 1.01 ± 0.74 mm respectively. In all divided regions except Teeth Region (TR), our model demonstrated equivalent performance to experienced CMF surgeons in landmarks detection (p > 0.05).ConclusionsThe developed model demonstrated excellent performance in detecting craniomaxillofacial landmarks when considering manual digitization work of expertise as benchmark. It is also verified that the region division pattern designed in this study remarkably improved the detection accuracy.

Advances in Ascorbic Acid (Vitamin C) Manufacturing: Green Extraction Techniques from Natural Sources

Ascorbic acid (AA), or vitamin C, is one of the most important vitamins consumed through the diet due to its critical role in many biological processes. Although the human body cannot synthesize it, AA is essential in maintaining healthy bodily structure, acting as a cofactor of many enzymes involved in collagen synthesis and an efficient immune system. At the same time, AA is used in the cosmetic field for its antioxidant and antipigmentary properties, in the food industry as additive, and in chemical synthesis as reducing agent. AA can be chemically synthesized, produced by the oxidative fermentation of bacteria, or extracted from natural sources. This review addresses the most recent developments in its manufacture, including techniques for extracting vitamin C from plants, fruits, vegetables, algae, and leaves, and focusing on the most commonly used green methods, i.e., ultrasound-assisted extraction, microwave-assisted extraction, pressurized liquid extraction, and supercritical fluid extraction. These methods are based on mild extraction conditions, environmentally friendly solvents, low time, cost, and energy consumption. In contrast, their extraction yields are comparable to or even higher than those of conventional methods.

TFF-CNN: Distributed optical fiber sensing intrusion detection framework based on two-dimensional multi-features

Distributed fiber optic vibration sensing system has been widely used in safety monitoring with distinct advantages, and the feature extraction and classification methods of fiber optic signals directly determine the real-time performance and reliability of the monitoring system. The existing research feature extraction methods are unidimensional and time consuming, which cannot balance the goals of high accuracy and low time consumption for safety monitoring systems. In this paper, we propose an efficient recognition framework for fusing signal time–frequency features, called TFF-CNN, based on Gramian Angular Difference Fields(GADF) and FFT co-generation matrix(FFTT) to manually extract two-dimensional time–frequency image features of fiber optic signals, taking the significant advantages of CNN in image processing and combining a two-channel model and a fusion module that simulates human decision-making behavior. The accuracy of TFF-CNN is 99.30 % and the detection response time is only 0.6 s. Compared with other methods in this field, TFF-CNN has the advantages of low false alarm rate and short time consumption, which is more suitable for deployment in security monitoring field with distributed fiber optic sensing system.Index Terms: Distributed optical fiber vibration sensing system(DVS), Two-dimensional multi-feature, CNN, intrusion detection, real-time monitoring.

DifFormer: Multi-Resolutional Differencing Transformer With Dynamic Ranging for Time Series Analysis.

Time series analysis is essential to many far-reaching applications of data science and statistics including economic and financial forecasting, surveillance, and automated business processing. Though being greatly successful of Transformer in computer vision and natural language processing, the potential of employing it as the general backbone in analyzing the ubiquitous times series data has not been fully released yet. Prior Transformer variants on time series highly rely on task-dependent designs and pre-assumed "pattern biases", revealing its insufficiency in representing nuanced seasonal, cyclic, and outlier patterns which are highly prevalent in time series. As a consequence, they can not generalize well to different time series analysis tasks. To tackle the challenges, we propose DifFormer, an effective and efficient Transformer architecture that can serve as a workhorse for a variety of time-series analysis tasks. DifFormer incorporates a novel multi-resolutional differencing mechanism, which is able to progressively and adaptively make nuanced yet meaningful changes prominent, meanwhile, the periodic or cyclic patterns can be dynamically captured with flexible lagging and dynamic ranging operations. Extensive experiments demonstrate DifFormer significantly outperforms state-of-the-art models on three essential time-series analysis tasks, including classification, regression, and forecasting. In addition to its superior performances, DifFormer also excels in efficiency - a linear time/memory complexity with empirically lower time consumption.

YOLO-FL: A target detection algorithm for reflective clothing wearing inspection

In recent years, target detection has been widely used in specific dress detection, to reduce the probability of accidents in certain fields such as construction sites, traffic management, etc. However, the detection of professional dress, especially reflective clothing, is mainly relied on manual detection so far, with the disadvantages of low efficiency and high time consumption. To this end, we focus on reflective clothing and propose the YOLO-FL detection algorithm based on YOLOV4-tiny. Firstly, a new intersection and union ratio calculation method KIoU is proposed and introduced into the original K-means++ clustering method to avoid the influence brought by distance in the original K-means++ clustering method. Secondly, the SPPF module is introduced into the neck network to make the feature information of different scales fully integrated and obtain a richer feature representation. Finally, the label smoothing strategy is used to optimize the loss function of the network model to prevent the network from overfitting and to improve the generalization ability of the model to a certain extent. We tested on the self-made reflective clothing dataset, and the mAP and F1 values of the YOLO-FL algorithm were 91.14% and 0.85, respectively, which improved by 8.29% and 0.12, respectively, compared with the original algorithm. In addition, experiments on PASCAL VOC and MS-COCO datasets show that the accuracy of the YOLO-FL algorithm has significantly improved (+4.77% on the Pascal VOC benchmark and +2.49% on the MS-COCO benchmark), and has excellent migration generalization performance.

Virtual lattice method for efficient Monte Carlo transport simulation of dispersion nuclear fuels

The randomly dispersed Tristructural-isotropic (TRISO) particle fuel is an attractive nuclear fuel type widely used in advanced reactor designs. However, the massive randomly-distributed fuel elements and the prohibitive computational costs pose challenges for high-fidelity modeling and simulation of dispersion fuels. To address this, we present a virtual lattice optimization method for accelerating the Monte Carlo particle transport simulation of nuclear systems with dispersion fuels. The new method involves using a regular overlaid mesh on the dispersed fuel region to improve geometry processing in Monte Carlo simulations. We describe the optimization methodology in our paper and construct a theoretical performance model to determine the optimal lattice pitch size that minimizes calculation cost. We then develop and implement the algorithm of the virtual method in the open-source Monte Carlo code OpenMC. To demonstrate the efficacy of the new algorithm, high-fidelity HTR-PM full core models based on the Shidao-Bay nuclear power plant are constructed, including an explicit representation of up to 420,000 fuel pebbles. Criticality and depletion simulations are performed on large-scale models. Results show that the k-effective derived by the optimized code agrees well with both the experiment and the original code. Further performance comparison shows that the virtual lattice method exhibits lower time consumption, higher parallel efficiency, and lower storage requirement in comparison to the conventional physical lattice method. The study establishes the effectiveness of the proposed methodology in facilitating efficient and accurate high-fidelity simulations of large-scale dispersion fuel models.

Comparison of Two Representative Methods for Differentiation of Human Induced Pluripotent Stem Cells into Mesenchymal Stromal Cells.

Mesenchymal stromal cells (MSCs) are adult pluripotent stem cells which have been widely used in regenerative medicine. As somatic tissue-derived MSCs are restricted by limited donation, quality variations, and biosafety, the past 10 years have seen a great rise in efforts to generate MSCs from human induced pluripotent stem cells (hiPSCs). Past and recent efforts in the differentiation of hiPSCs into MSCs have been centered around two culture methodologies: (1) the formation of embryoid bodies (EBs) and (2) the use of monolayer culture. This protocol describes these two representative methods in deriving MSC from hiPSCs. Each method presents its advantages and disadvantages, including time, cost, cell proliferation ability, the expression of MSC markers, and their capability of differentiation in vitro. This protocol demonstrates that both methods can derive mature and functional MSCs from hiPSCs. The monolayer method is characterized by lower cost, simpler operation, and easier osteogenic differentiation, while the EB method is characterized by lower time consumption.

Wind Turbine Surface Defect Detection Method Based on YOLOv5s-L

In order to solve the problems of low efficiency, time consumption and high costs in the detection of defects on wind turbine surfaces in industrial scenarios, an improved YOLOv5 algorithm for wind turbine surface defect detection is proposed, named YOLOv5s-L. Firstly, the C3 module of YOLOv5s is replaced with the C2f module, which is more abundant in gradient flow, to enhance the ability of feature extraction and feature fusion. Secondly, the Squeeze and Excitation (SE) module is embedded in the YOLOv5 Backbone network to filter out redundant feature information and retain important feature information. Thirdly, the weighted Bidirectional Feature Pyramid Network (BiFPN) is introduced to replace the FPN + PAN, which can achieve a higher level of feature fusion while keeping the weight light. Finally, the Focal Loss function is used to replace the CIOU Loss function of the YOLOv5 algorithm to optimize the training model and improve the accuracy of the algorithm. The experimental results show that, compared with the traditional YOLOv5 algorithm, the average precision mAP is improved by 1.9%, and the frame rate FPS can reach 145 F/s without increasing the model parameters; it can satisfy the requirements for real-time, accurate detection on mobile devices. This method provides effective support for surface defect detection of wind turbines and provides reference for intelligent wind farm operation and maintenance.

A visually meaningful image encryption algorithm based on P-tensor product compressive sensing and newly-designed 2D memristive chaotic map

The noise-like visual feature of cipher images produced by using the traditional image encryption technology explicitly reflects the presence of secret information. To overcome this issue, a visually meaningful image encryption algorithm is proposed based on a newly designed 2D memristive chaotic map, P-tensor product compressive sensing (PTP-CS) and discrete Hartley transform (DHT). For concreteness, a new two-dimensional discrete memristive chaotic map is first designed to provide highly unpredictable secret code streams while maintaining low time consumption. Second, the threshold processing and zigzag confusion operations are performed on the discrete wavelet coefficients of the plain image to meet the prerequisites for effective compression. Third, the intermediate secret information is obtained by utilizing PTP-CS in the compression layer. Information entropy and edge entropy are employed to adaptively identify the complex regions that are suitable for embedding due to inconspicuous visual degradation in the carrier image. Finally, the embedding of the secret information in the DHT domain of these regions is accomplished. Security test and performance analysis confirm that our algorithm has the advantage of a high balance between the encryption security and the decryption recovery, and exhibits excellent performance in important indicators such as visual quality, robustness and timeliness.

Dynamic response and dynamic buckling of general laminated plates: A semi-inverse method

In this paper, a simplified way of deriving the equation of motion is presented for the general laminated composite plates under time-dependent compression. The considered plates with simply supported boundary conditions are subjected to the in-plane harmonic loads. By employing a novel semi-inverse method (SIM) based on least square (LS) fitting, the equation of motion is derived from the static equilibrium path. Dynamic responses of glass fiber reinforced polymer (GFRP) laminated plates with different layer arrangements are analyzed by such equations and the dynamic buckling loads are obtained based on Budiansky–Hutchinson and Volmir criteria. Static equilibrium paths are obtained based on commercial finite element method (FEM), i.e., ANSYS software. Again, the results of SIM are compared with those of FEM in ANSYS software and the verifications of dynamic responses are proved considering full types of material couplings. The presented results show that the proposed simplified way of the derivation of the equation of motion allows for a very low time consumption analysis.

Electromagnetic Performance Analysis of a Field-Modulated Permanent Magnet Motor Using Improved Hybrid Subdomain Method

The hybrid subdomain method (HSDM) combining conventional SDM with equivalent magnetic network has been proved to be a qualified approach to field prediction. However, this method still suffers two issues: first, the saturation effect of stator core is considered but that of rotor core is ignored, thus causing no-accuracy calculation; second, the mathematical complexity is inevitable due to adoption of virtual line-current density as extra interface conditions. This work is to develop an improved HSDM (IHSDM) for accurately and efficiently predicting the electromagnetic performance of a field-modulated permanent-magnet motor. The key of the proposed IHSDM is to fully consider magnetic saturation inside both stator and rotor cores. More importantly, the replacement of virtual-line current density by virtual-surface magnetization and current density makes interface conditions as simple as the SDM. The quantitative comparisons with conventional HSDM and FEA are conducted and the prototyping tests are also performed. It shows that the proposed IHSDM has lower time consumption and occupied memory, and electromagnetic quantities have about two times more accuracy than conventional HSDM. Additionally, a dynamic calculation is achieved by the IHSDM and verified by FEA.

Radar specific emitter identification via the Attention-GRU model

In radar specific emitter identification (SEI), various types of unintentional modulation on pulse (UMOP) are selected as the features for discriminating between different radars. Unintentional Phase Modulation on Pulse (UPMOP), a typical type of UMOP, can provide crucial information for identifying radars. In most radar SEI algorithms, sacrificing time efficiency for higher accuracy is a common trade-off. This paper proposes a method to solve this problem by combining denoised UPMOP sequences with an Attention-based Gated Recurrent Units (Attention-GRU) model, which showed an excellent performance. Firstly, the cause of UPMOP is analyzed and the phase observation model of radar emitter signals and mathematical model of UPMOP are given. Then, the least-squares method is used to eliminate the linear trend of the phase observation model and obtain a noised estimation of the UPMOP sequences. Thirdly, the uniform B-spline (UBS) curves are then used to fit the noised estimation, resulting in a denoised and refined UPMOP sequence. Finally, the Attention-GRU model is employed to extract features from the denoised UPMOP sequences to identify radar emitters automatically. Results from simulation and measured data experiments show that the overall recognition rate of the algorithm reaches over 93% and the algorithm has excellent performance, with high identification accuracy and relatively low time consumption, even in low signal-to-noise ratio (SNR) conditions.

CI_GRU: An efficient DGA botnet classification model based on an attention recurrence plot

Malware is often embedded with domain generation algorithms (DGAs) to prevent firewall interception and domain black-and-white list comparison detection while hiding command and control (C&C) servers to tighten the control of botnets. DGA domains are diverse and difficult to obtain, resulting in highly unbalanced datasets. Domain names generated by different DGA families do not differ much at the sequence data level and it is difficult to extract their features. The above characteristics lead to poor accuracy, poor generalization ability, and bloatedness of DGA domain name classification models based on deep learning. To solve the above problems, the visual representation of sequence data and the DGA domain classification model are presented in this paper. First, the DGA domain name is mapped to the attention recurrence plot (Att_RP) proposed in this paper, which can enrich the data phase space features and differentiate the key phase space features. After that, Att_RP is sent to a DGA domain name classification model (CI_GRU) proposed in this paper for data dimension transformation processing, followed by classification. Experiments show that the classification accuracy, F1_score, and recall of the model for a variety of DGA families in the wild are higher than 99%, and can also accurately classify four types of crafted DGA families. Compared with similar models, the model has high classification accuracy, low time consumption, low generalization error, and high efficiency, and the size of the model is less than one-tenth of similar models.

A non-iterative foveated single-pixel imaging using fast transformation algorithm

Inspired by the human eye retina, foveated single-pixel imaging employs an iterative algorithm, which, unfortunately, poses a challenge for real-time imaging due to its time-consuming nature. To address this issue, we present a non-iterative foveated single-pixel imaging method based on a computationally fast algorithm, reducing time consumption by five orders of magnitude. The fast algorithmic transform facilitates the acquisition of foveated reconstructions. In addition to mimicking human saccadic eye movements, the foveated strategy adapts to motion tracking through the foveal gaze control method. During high-resolution imaging in the fovea area, the maximum sensing area expands 3.14 times, similar to human vision. The proposed retina-like single-pixel imaging has great potential in future applications with the advantages of avoiding complex optics design, low time consumption, and wider vision field.

Defect detection and classification on semiconductor wafers using two-stage geometric transformation-based data augmentation and SqueezeNet lightweight convolutional neural network

The manufacturing industry is evolving in line with the principles of Industry 4.0, with the aim of achieving higher levels of automation and digitization. In particular, deep learning algorithms such as convolutional neural networks (CNNs) are key enabling technologies to achieve this goal. The semiconductor industry is a particular case where CNNs are used to assist inspection systems and human operators in defect classification of wafers. However, deep CNN models are time consuming and resource intensive. It is therefore necessary to look for alternatives. One of these alternatives is the use of lightweight models, which provide competitive classification performance with low time and resource consumption. Therefore, the motivation of this work is to apply these lightweight models to semiconductor defect classification and compare their performance with that obtained by deep CNN models in similar work. In this line, this paper introduces an efficient two-step approach combining traditional computer vision techniques and a lightweight SqueezeNet CNN for defect detection and classification. The lightweight SqueezeNet model is tuned using a grid search algorithm. After obtaining the optimal model, its metrics are presented and compared with results from related work. Using a semiconductor surface defect dataset from a multinational semiconductor company, our lightweight model can achieve really competitive classification results (99.356% versus 99.443% obtained by ResNet50) while consuming significantly less time than other heavyweight models (80.146% less time than ResNet50).

A variable dimensional chaotic map-based medical image encryption algorithm with multi-mode.

Since the COVID-19 pandemic, telemedicine or non-face-to-face medicine has increased significantly. In practice, various types of medical images are essential to achieve effective telemedicine. Medical image encryption algorithms play an irreplaceable role in the fast and secure transmission and storage of these medical images. However, most of the existing medical image encryption algorithms are full encryption algorithms, which are inefficient and time-consuming, so they are not suitable for emergency medical scenarios. To improve the efficiency of encryption, a small number of works have focused on partial or selective encryption algorithms for medical images, in which different levels of encryption strategies were adopted for different information content regions of medical images. However, these encryption algorithms have inadequate security more or less. In this paper, based on the Logistic map, we designed an improved variable dimension map. Then, an encryption algorithm for medical images was proposed based on it. This algorithm has two modes: (1) full encryption mode and (2) semi-full encryption mode, which can better adapt to different medical scenarios, respectively. In full encryption mode, all pixels of medical images are encrypted by using the confusion-diffusion structure. In semi-full encryption mode, the region of interest of medical images is extracted. The confusion was first adopted to encrypt the region of interest, and then, the diffusion was adopted to encrypt the entire image. In addition, no matter which encryption mode is used, the algorithm provides the function of medical image integrity verification. The proposed algorithm was simulated and analyzed to evaluate its effectiveness. The results show that in semi-full encryption mode, the algorithm has good security performance and lower time consumption; while in full encryption mode, the algorithm has better security performance and is acceptable in time.