Research on Facial Fatigue Detection of Drivers with Multi-feature Fusion

In order to solve the shortcomings of current fatigue detection methods such as low accuracy or poor real-time performance,a fatigue detection method based on multi-feature fusion is proposed.Firstly,the HOG face detection algorithm and KCF target tracking algorithm are integrated and deformable convolutional neural network is introduced to identify the state of extracted eyes and mouth,fast track the detected faces and extract continuous and stable target faces for more efficient extraction.Then the head pose algorithm is introduced to detect the driver’s head in real time and obtain the driver’s head state information.Finally,a multi-feature fusion fatigue detection method is proposed based on the state of the eyes,mouth and head.According to the experimental results,the proposed method can detect the driver’s fatigue state in real time with high accuracy and good robustness compared with the current fatigue detection algorithms.

A Moving IR Point Target Detection Algorithm Based on Reverse Phase Feature of Neighborhood in Difference Between Neighbor Frame Images

An algorithm for detecting moving IR point target in complex background is proposed, which is based on the Reverse Phase Feature of Neighborhood （RPFN） of target in difference between neighbor frame images that two positions of the target in the difference image are near and the gray values of them are close to in absolute value but with inverse sign. Firstly, pairs of points with RPFN are detected in the difference image between neighbor frame images, with which a virtual vector graph is made, and then the moving point target can be detected by the vectors＇ sequence cumulated in vector graphs. In addition, a theorem for the convergence of detection of target contrail by this algorithm is given and proved so as to afford a solid guarantee for practical applications of the algorithm proposed in this paper. Finally, some simulation results with 1000 frames from 10 typical images in complex background show that moving point targets with SNR not lower than 1.5 can be detected effectively.

Smoke root detection from video sequences based on multi-feature fusion

Smoke detection is the most commonly used method in early warning of fire and is widely used in forest detection.Most existing smoke detection methods contain empty spaces and obstacles which interfere with detection and extract false smoke roots.This study developed a new smoke roots search algorithm based on a multi-feature fusion dynamic extraction strategy.This determines smoke origin candidate points and region based on a multi-frame discrete confidence level.The results show that the new method provides a more complete smoke contour with no background interference,compared to the results using existing methods.Unlike video-based methods that rely on continuous frames,an adaptive threshold method was developed to build the judgment image set composed of non-consecutive frames.The smoke roots origin search algorithm increased the detection rate and significantly reduced false detection rate compared to existing methods.

ADS-B Anomaly Data Detection Model Based on Deep Learning and Difference of Gaussian Approach

Due to the influence of terrain structure,meteorological conditions and various factors,there are anomalous data in automatic dependent surveillance-broadcast(ADS-B)message.The ADS-B equipment can be used for positioning of general aviation aircraft.Aim to acquire the accurate position information of aircraft and detect anomaly data,the ADS-B anomaly data detection model based on deep learning and difference of Gaussian(DoG)approach is proposed.First,according to the characteristic of ADS-B data,the ADS-B position data are transformed into the coordinate system.And the origin of the coordinate system is set up as the take-off point.Then,based on the kinematic principle,the ADS-B anomaly data can be removed.Moreover,the details of the ADS-B position data can be got by the DoG approach.Finally,the long short-term memory(LSTM)neural network is used to optimize the recurrent neural network(RNN)with severe gradient reduction for processing ADS-B data.The position data of ADS-B are reconstructed by the sequence to sequence(seq2seq)model which is composed of LSTM neural network,and the reconstruction error is used to detect the anomalous data.Based on the real flight data of general aviation aircraft,the simulation results show that the anomaly data can be detected effectively by the proposed method of reconstructing ADS-B data with the seq2seq model,and its running time is reduced.Compared with the RNN,the accuracy of anomaly detection is increased by 2.7%.The performance of the proposed model is better than that of the traditional anomaly detection models.

On-Line Detection of EDM Spark Locations Using Potential Difference Method

The valuation relation of potential difference with discharging time in Electrical Discharge Machining (EDM) is analyzed theoretically and tested and verified by experiments designed in this paper and the relation between potential difference and spark location is induced and analyzed, and proceed by experiments under the condition of onedimension.

Evaluating the Effect of Tillage on Carbon Sequestration Using the Minimum Detectable Difference Concept

Three long-term field trials in humid regions of Canada and the USA were used to evaluate the influence of soil depth and sample numbers on soil organic carbon (SOC) sequestration in no-tillage (NT) and moldboard plow (MP) corn (Zea mays L.) and soybean (Glycine max L.) production systems. The first trial was conducted on a Maryhill silt loam (Typic Hapludalf) at Elora, Ontario, Canada, the second on a Brookston clay loam (Typic Argiaquoll) at Woodslee, Ontario, Canada, and the third on a Thorp silt loam (Argiaquic Argialboll) at Urbana, Illinois, USA. No-tillage led to significantly higher SOC concentrations in the top 5 cm compared to MP at all 3 sites. However, NT resulted in significantly lower SOC in sub-surface soils as compared to MP at Woodslee (10-20 cm, P = 0.01) and Urbana (20-30 cm, P < 0.10). No-tillage had significantly more SOC storage than MP at the Elora site (3.3 Mg C ha-1) and at the Woodslee site (6.2 Mg C ha-1) on an equivalent mass basis (1350 Mg ha-1 soil equivalent mass). Similarly, NT had greater SOC storage than MP at the Urbana site (2.7 Mg C ha-1) on an equivalent mass basis of 675 Mg ha-1 soil. However, these differences disappeared when the entire plow layer was evaluated for both the Woodslee and Urbana sites as a result of the higher SOC concentrations in MP than in NT at depth. Using the minimum detectable difference technique, we observed that up to 1500 soil sample per tillage treatment comparison will have to be collected and analyzed for the Elora and Woodslee sites and over 40 soil samples per tillage treatment comparison for the Urbana to statistically separate significant differences in the SOC contents of sub-plow depth soils. Therefore, it is impracticable, and at the least prohibitively expensive, to detect tillage-induced differences in soil C beyond the plow layer in various soils.

CAFUNeT:A small infrared target detection method in complex backgrounds

Small infrared target detection has widespread applications in various fields including military,aviation,and medicine.However,detecting small infrared targets in complex backgrounds remains challenging.To detect small infrared targets,we propose a variable-structure U-shaped network referred as CAFUNet.A central differential convolution-based encoder,ASPP,an Attention Fusion module,and a decoder module are the critical components of the CAFUNet.The encoder module based on central difference convolution effectively extracts shallow detail information from infrared images,complemented by rich contextual information obtained from the deep features in the decoder module.However,the direct fusion of the shallow detail features with semantic features may lead to feature mismatch.To address this,we incorporate an Attention Fusion(AF)module to enhance the network performance further.We performed ablation studies on each module to evaluate its effectiveness.The results show that our proposed algorithm outperforms the state-of-the-art methods on publicly available datasets.

SAR Change Detection Algorithm Combined with FFDNet Spatial Denoising

Objectives:When detecting changes in synthetic aperture radar(SAR)images,the quality of the difference map has an important impact on the detection results,and the speckle noise in the image interferes with the extraction of change information.In order to improve the detection accuracy of SAR image change detection and improve the quality of the difference map,this paper proposes a method that combines the popular deep neural network with the clustering algorithm.Methods:Firstly,the SAR image with speckle noise was constructed,and the FFDNet architecture was used to retrain the SAR image,and the network parameters with better effect on speckle noise suppression were obtained.Then the log ratio operator is generated by using the reconstructed image output from the network.Finally,K-means and FCM clustering algorithms are used to analyze the difference images,and the binary map of change detection results is generated.Results:The experimental results have high detection accuracy on Bern and Sulzberger’s real data,which proves the effectiveness of the method.

Clinical Application of Sex Hormone in Different Physiological Periods in the Diagnosis of Infertility Patients

Background: Infertility is characterized by the inability to conceive after a year of regular unprotected intercourse. Aims: This study aimed to investigate the diagnostic value of sex hormone levels during different physiological periods in the diagnosis of infertility patients. Methods: From December 2019 to May 2021, a total of 93 infertility patients were admitted and selected as the observation group. Among them, 31 cases were in the follicular stage, 31 cases in the ovulation stage, and 31 cases in the luteal stage. Ninety-three healthy women for fertility evaluation due to male infertility were selected as the control group. The control group included 31 women in the follicular phase, 31 women in the ovulatory phase, and 31 women in the luteal phase. The levels of sex hormones (prolactin (PRL), luteinizing hormone (LH), follicle-stimulating hormone (FSH), estradiol (E2), testosterone (T), and progesterone (P)) during different physiological phases were compared between the observation and control groups. Results: The follicular phase showed no significant difference in LH levels between the observation group and the control group. The observation group showed higher levels of PRL and P compared to the control group, while the levels of FSH, E2, and T were lower in the observation group compared to the control group. The ovulation phase showed no significant difference in PRL levels between the two groups. The observation group showed lower levels of LH, FSH, E2, T, and P compared to the control group. The luteal phase showed no statistical difference in E2 levels between the two groups. The observation group showed higher levels of PRL, LH, and FSH compared to the control group, while the levels of T and P were lower in the observation group compared to the control group. Conclusion: Infertile women show variations in hormone levels compared to the normal levels during the follicular phase, ovulatory phase, and luteal phase.

Comparison of detection results of hypoxic-ischemic encephalopathy at different degrees in infant patients between brain electrical activity mapping, transcranial Doppler sonography and computer tomography examinations

BACKGROUND： It has been proved that brain electrical activity mapping （BEAM） and transcranial Doppler （TCD） detection can reflect the function of brain cell and its diseased degree of infant patients with moderate to severe hypoxic-ischemic encephalopathy （HIE）. OBJECTIVE： To observe the abnormal results of HIE at different degrees detected with BEAM and TCD in infant patients, and compare the detection results at the same time point between BEAM, TCD and computer tomography （CT） examinations. DESIGN ： Contrast observation SETTING： Departments of Neuro-electrophysiology and Pediatrics, Second Affiliated Hospital of Qiqihar Medical College. PARTICIPANTS： Totally 416 infant patients with HIE who received treatment in the Department of Newborn Infants, Second Affiliated Hospital of Qiqihar Medical College during January 2001 and December 2005. The infant patients, 278 male and 138 female, were at embryonic 37 to 42 weeks and weighing 2.0 to 4.1 kg, and they were diagnosed with CT and met the diagnostic criteria of HIE of newborn infants compiled by Department of Neonatology, Pediatric Academy, Chinese Medical Association. According to diagnostic criteria, 130 patients were mild abnormal, 196 moderate abnormal and 90 severe abnormal. The relatives of all the infant patients were informed of the experiment. METHOOS： BEAM and TCD examinations were performed in the involved 416 infant patients with HIE at different degrees with DYD2000 16-channel BEAM instrument and EME-2000 ultrasonograph before preliminary diagnosis treatment （within 1 month after birth） and 1,3,6,12 and 24 months after birth, and detected results were compared between BEAM, TCD and CT examinations. MAIN OUTCOME MEASURES： Comparison of detection results of HIE at different time points in infant patients between BEAM. TCD and CT examinations. RESULTS： All the 416 infant patients with HIE participated in the result analysis. （1） Comparison of the detected results in infant patients with mild HIE at different time points after birth between BEAM, TCD and CT examinations： BEAM examination showed that the recovery was delayed, and the abnormal rate of BEAM examination was significantly higher than that of CT examination 1 and 3 months after birth [55.4%（72/130）vs. 17.0% （22/130 ）,x^2=41.66 ;29.2% （ 38/130 ） vs. 6.2% （ 8/130 ）, x^2=23.77, P 〈 0.01 ], exceptional patients had mild abnormality and reached the normal level in about 6 months. TCD examination showed that the disease condition significantly improved and infant patients with HIE basically recovered 1 or 2 months after birth, while CT examination showed that infant patients recovered 3 or 4 months after birth. （2） Comparison of detection results of infant patients with moderate HIE at different time points between BEAM, TCD and CT examinations： The abnormal rate of BEAM examination was significantly higher than that of CT examination 1,3,6 and 12 months after birth [90.8% （178/196）,78.6% （154/196）,x^2=4.32,P 〈 0.05;64.3% （126/196）,43.9% （86/196） ,x^2=16.44 ;44.9% （88/196） ,22.4% （44/196）,x^2=22.11 ;21.4% （42/196）, 10.2% （20/196）,x^2=9.27, P 〈 0.01]. BEAM examination showed that there was still one patient who did not completely recovered in the 24^th month due to the relatives of infant patients did not combine the treatment,. TCD examination showed that the abnormal rate was 23.1%（30/196）in the 1^st month after birth, and all the patients recovered to the normal in the 3^rd month after birth, while CT examination showed that mild abnormality still existed in the 24^th month after birth （1.0% ,2/196）. （3） Comparison of detection results of infant patients with severe HIE at different time points between BEAM, TCD and CT examinations： The abnormal rate of BEAM examination was significantly higher than that of CT examination in the 1^st, 3^rd, 6^th and 12^th months after birth[86.7% （78/90）,44.4% （40/90）,x^2=35.53;62.2% （56/90）,31.1% （28/90）,x^2=17.51 ;37.8% （34/90）,6.7% （6/90）, x^2=27.14, P 〈 0.01]. BEAM examination showed that mild abnormality still existed in 4 infant patients in the 24^th month after birth. TCD examination showed that the abnormal rate was 11.1% （10/90） in the 3^rd month after birth, and all the infant patients recovered in the 6^th month after birth. CT examination showed that the abnormal rate was 6.7%（6/90） in the 12^th month after birth, and all of infant patients recovered to the normal in the 24^th month after birth.CONCLUSION ： BEAM is the direct index to detect brain function of infant patients with HIE, and positive reaction is still very sensitive in the tracking detection of convalescent period. The positive rate of morphological reaction in CT examination is superior to that in TCD examination, and the positive rate is very high in the acute period of HIE in examination.

Fracture identification based on remote detection acoustic reflection logging

Fracture identification is important for the evaluation of carbonate reservoirs. However, conventional logging equipment has small depth of investigation and cannot detect rock fractures more than three meters away from the borehole. Remote acoustic logging uses phase-controlled array-transmitting and long sound probes that increase the depth of investigation. The interpretation of logging data with respect to fractures is typically guided by practical experience rather than theory and is often ambiguous. We use remote acoustic reflection logging data and high-order finite-difference approximations in the forward modeling and prestack reverse-time migration to image fractures. First, we perform forward modeling of the fracture responses as a function of the fracture-borehole wall distance, aperture, and dip angle. Second, we extract the energy intensity within the imaging area to determine whether the fracture can be identified as the formation velocity is varied. Finally, we evaluate the effect of the fracture-borehole distance, fracture aperture, and dip angle on fracture identification.

TDOA-based Sybil attack detection scheme for wireless sensor networks

As wireless sensor networks （WSN） are deployed in fire monitoring, object tracking applications, security emerges as a central requirement. A case that Sybil node illegitimately reports messages to the master node with multiple non-existent identities （ID） will cause harmful effects on decision-making or resource allocation in these applications. In this paper, we present an efficient and lightweight solution for Sybil attack detection based on the time difference of arrival （TDOA） between the source node and beacon nodes. This solution can detect the existence of Sybil attacks, and locate the Sybil nodes. We demonstrate efficiency of the solution through experiments. The experiments show that this solution can detect all Sybil attack cases without missing.

Identifying influential nodes in social networks via community structure and influence distribution difference

This paper aims to effectively solve the problem of the influence maximization in social networks.For this purpose,an influence maximization method that can identify influential nodes via the community structure and the influence distribution difference is proposed.Firstly,the network embedding-based community detection approach is developed,by which the social network is divided into several high-quality communities.Secondly,the solution of influence maximization is composed of the candidate stage and the greedy stage.The candidate stage is to select candidate nodes from the interior and the boundary of each community using a heuristic algorithm,and the greedy stage is to determine seed nodes with the largest marginal influence increment from the candidate set through the sub-modular property-based Greedy algorithm.Finally,experimental results demonstrate the superiority of the proposed method compared with existing methods,from which one can further find that our work can achieve a good tradeoff between the influence spread and the running time.

Automatic Detection of Moving Target with an Active Camera

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Establishing minimum clinically important difference values for the Patient-Reported Outcomes Measurement Information System Physical Function, hip disability and osteoarthritis outcome score for joint reconstruction, and knee injury and osteoarthritis out

AIM To establish minimum clinically important difference(MCID) for measurements in an orthopaedic patient population with joint disorders.METHODS Adult patients aged 18 years and older seeking care for joint conditions at an orthopaedic clinic took the Patient-Reported Outcomes Measurement Information System Physical Function(PROMIS~? PF) computerized adaptive test(CAT), hip disability and osteoarthritis outcome score for joint reconstruction(HOOS JR), and the knee injury and osteoarthritis outcome score for joint reconstruction(KOOS JR) from February 2014 to April 2017. MCIDs were calculated using anchorbased and distribution-based methods. Patient reports of meaningful change in function since their first clinic encounter were used as an anchor.RESULTS There were 2226 patients who participated with a mean age of 61.16(SD = 12.84) years, 41.6% male, and 89.7% Caucasian. Mean change ranged from 7.29 to 8.41 for the PROMIS~? PF CAT, from 14.81 to 19.68 for the HOOS JR, and from 14.51 to 18.85 for the KOOS JR. ROC cut-offs ranged from 1.97-8.18 for the PF CAT, 6.33-43.36 for the HOOS JR, and 2.21-8.16 for the KOOS JR. Distribution-based methods estimated MCID values ranging from 2.45 to 21.55 for the PROMIS~? PF CAT; from 3.90 to 43.61 for the HOOS JR, and from 3.98 to 40.67 for the KOOS JR. The median MCID value in the range was similar to the mean change score for each measure and was 7.9 for the PF CAT, 18.0 for the HOOS JR, and 15.1 for the KOOS JR.CONCLUSION This is the first comprehensive study providing a wide range of MCIDs for the PROMIS? PF, HOOS JR, and KOOS JR in orthopaedic patients with joint ailments.

Moving Target Detection and Tracking for Smartphone Automatic Focusing

In this paper,a non-contact auto-focusing method is proposed for the essential function of auto-focusing in mobile devices.Firstly,we introduce an effective target detection method combining the 3-frame difference algorithm and Gauss mixture model,which is robust for complex and changing background.Secondly,a stable tracking method is proposed using the local binary patter feature and camshift tracker.Auto-focusing is achieved by using the coordinate obtained during the detection and tracking procedure.Experiments show that the proposed method can deal with complex and changing background.When there exist multiple moving objects,the proposed method also has good detection and tracking performance.The proposed method implements high efficiency,which means it can be easily used in real mobile device systems.

Algorithm Research on Moving Object Detection of Surveillance Video Sequence

In video surveillance, there are many interference factors such as target changes, complex scenes, and target deformation in the moving object tracking. In order to resolve this issue, based on the comparative analysis of several common moving object detection methods, a moving object detection and recognition algorithm combined frame difference with background subtraction is presented in this paper. In the algorithm, we first calculate the average of the values of the gray of the continuous multi-frame image in the dynamic image, and then get background image obtained by the statistical average of the continuous image sequence, that is, the continuous interception of the N-frame images are summed, and find the average. In this case, weight of object information has been increasing, and also restrains the static background. Eventually the motion detection image contains both the target contour and more target information of the target contour point from the background image, so as to achieve separating the moving target from the image. The simulation results show the effectiveness of the proposed algorithm.

An Improved Moving Object Detection Algorithm Based on Gaussian Mixture Models

Aiming at the problems that the classical Gaussian mixture model is unable to detect the complete moving object, and is sensitive to the light mutation scenes and so on, an improved algorithm is proposed for moving object detection based on Gaussian mixture model and three-frame difference method. In the process of extracting the moving region, the improved three-frame difference method uses the dynamic segmentation threshold and edge detection technology, and it is first used to solve the problems such as the illumination mutation and the discontinuity of the target edge. Then, a new adaptive selection strategy of the number of Gaussian distributions is introduced to reduce the processing time and improve accuracy of detection. Finally, HSV color space is used to remove shadow regions, and the whole moving object is detected. Experimental results show that the proposed algorithm can detect moving objects in various situations effectively.

Magnetic Barkhausen Noise Profile Analysis: Effect of Excitation Field Strength and Detection Coil Sensitivity in Case Carburized Steel

The sensitivity of magnetic Barkhausen noise (MBN) profile to changes in the excitation field strength and the number of turns of the detection coil was investigated in inhomogeneous material. Generally, the 0.5 mm case depth EN 36 steel specimen shows a double peak profile indicative of inhomogeneity through the detected depth the magnetized landscape. Various excitation field amplitudes were applied to the specimen and the induced MBN emission was analyzed for each magnetizing current. Excitation field at the lowest level induced an MBN emission of two peaks of equivalent heights. The first peak occurs at lower field than the second peak in the magnetization period. As the excitation field increased, the height of both peaks increased but the second peak increased in a higher rate than that of the first peak. Beyond certain excitation level, both peaks began to saturate and no increase in the MBN intensity was observed with increased excitation field strength. However, peak position and the number of Barkhausen events, indicated linearly as a function of the applied field strength. The experiment also establishes that the number of turns in the detection coil is important parameter which controls the height of the first peak. Low field peak height increases as the number of turn of the detection coil increases. The results indicate that the potential difference applied to the electromagnet and the sensitivity of the detection coil, determine the MBN profile characteristics.

Acoustic field simulations of logging while drilling by cylindrical finite difference with variable grids

This study proposes an elastic finite difference(FD)time domain method with variable grids in three-dimensional cylindrical coordinates.The calculations will diverge and become less accurate by conventional cylindrical FD as the grid size gradually becomes more extensive with the increasing radius.To prevent grids from being too coarse in far fields,we compensate for the grid cell infl ation by refi ning the grid step in the azimuthal direction.The variable grid FD in the cylindrical coordinate systems has a higher effi ciency in solving acoustic logging while drilling(LWD)problems because the grid boundaries are consistent with those of the drill collar and the borehole.The proposed algorithm saves approximately 94%of the FD grids,80%of the computation time,and memory with a higher calculation accuracy than the FD on rectangular grids for the same models.We also calculate the acoustic LWD responses of the fl uid-fi lled borehole intersecting with fractures.Refl ections are generated at the fractures,which can be equivalent to an additional scattering source.The mode conversions between the collar and the Stoneley waves are revealed.The Stoneley spectra are more sensitive to the fracture.Finally,the logs in a heterogeneous formation with two refl ectors far from the borehole are modeled,and a means of estimating the azimuth of geological interfaces from refl ections is proposed.