An Improved Solov2 Based on Attention Mechanism and Weighted Loss Function for Electrical Equipment Instance Segmentation

The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low precision.Because of the reliable,safe and easy-to-operate technology provided by deep learning-based video surveillance for unmanned inspection of electrical equipment,this paper uses the bottleneck attention module(BAM)attention mechanism to improve the Solov2 model and proposes a new electrical equipment segmentation mode.Firstly,the BAM attention mechanism is integrated into the feature extraction network to adaptively learn the correlation between feature channels,thereby improving the expression ability of the feature map;secondly,the weighted sum of CrossEntropy Loss and Dice loss is designed as the mask loss to improve the segmentation accuracy and robustness of the model;finally,the non-maximal suppression(NMS)algorithm to better handle the overlap problem in instance segmentation.Experimental results show that the proposed method achieves an average segmentation accuracy of mAP of 80.4% on three types of electrical equipment datasets,including transformers,insulators and voltage transformers,which improve the detection accuracy by more than 5.7% compared with the original Solov2 model.The segmentation model proposed can provide a focusing technical means for the intelligent management of power systems.

Brain networks in newborns and infants with and without sensorineural hearing loss:A functional near-infrared spectroscopy study

BACKGROUND Understanding the impact of early sensory deficits on brain development is essential for understanding developmental processes and developing potential interventions.While previous studies have looked into the impact of prenatal experiences on language development,there is a lack of research on how these experiences affect early language and brain function development in individuals with sensorineural hearing loss(SNHL).AIM To investigate SNHL effects on early brain development and connectivity in 4-month-olds vs healthy newborns and controls.METHODS The research involved analyzing the functional brain networks of 65 infants,categorized into three groups:28 healthy newborns,224-month-old participants with SNHL,and 15 age-matched healthy participants.The resting-state functional connectivity was measured and compared between the groups using functional near-infrared spectroscopy and graph theory to assess the brain network properties.RESULTS Significant differences were found in resting-state functional connectivity between participants with SNHL and age-matched controls,indicating a developmental lag in brain connectivity for those with SNHL.Surprisingly,SNHL participants showed better connectivity development compared to healthy newborns,with connectivity strengths of 0.13±0.04 for SNHL,0.16±0.08 for controls,and 0.098±0.04 for newborns.Graph theory analysis revealed enhanced global brain network properties for the SNHL group,suggesting higher communication efficiency at 4 months.No significant differences were noted in network properties between 4-month-old SNHL participants and neonates.A unique pattern of central hubs was observed in the SNHL group,with 2 hubs in the left hemisphere compared to 6 in controls.CONCLUSION 4-month-old infants with SNHL have a distinct brain network pattern with efficient long-distance information transmission but less effective local communication compared to age-matched controls.

Sound Transmission Loss Analysis of a Double Plate-Acoustic Cavity Coupling System with In-Plane Functionally Graded Materials

In this paper, the isogeometric analysis (IGA) is employed to develop an acoustic radiation model for a double plate-acoustic cavity coupling system, with a focus on analyzing the sound transmission loss (STL). The functionally graded (FG) plate exhibits a different material properties in-plane, and the power-law rule is adopted as the governing principle for material mixing. To validate the harmonic response and demonstrate the accuracy and convergence of the isogeometric modeling, ANASYS is utilized to compare with numerical examples. A plane wave serves as the acoustic excitation, and the Rayleigh integral is applied to discretize the radiated plate. The STL results are compared with the literature, confirming the reliability of the coupling system. Finally, the investigation is conducted to study impact of cavity depth and power-law parameter on the STL.

Vibration and sound transmission loss characteristics of porous foam functionally graded sandwich panels in thermal environment

This study investigates the vibration and acoustic properties of porous foam functionally graded(FG)plates under the influence of the temperature field.The dynamics equations of the system are established based on Hamilton's principle by using the higher-order shear deformation theory under the linear displacement-strain assumption.The displacement shape function is assumed according to the four-sided simply-supported(SSSS)boundary condition,and the characteristic equations of the system are derived by combining the motion control equations.The theoretical model of vibro-acoustic coupling is established by using the acoustic theory and fluid-structure coupling solution method under the simple harmonic acoustic wave.The system's natural frequency and sound transmission loss(STL)are obtained through programming calculations and compared with the literature and COMSOL simulation to verify the validity and reliability of the theoretical model.The effects of various factors,such as temperature,porosity coefficients,gradient index,core thickness,width-to-thickness ratio on the vibration,and STL characteristics of the system,are discussed.The results provide a theoretical basis for the application of porous foam FG plates in engineering to optimize vibration and sound transmission properties.

Effect of foot reflexology on an infant with sensorineural hearing loss:A case report

BACKGROUND Our study contributes to the further understanding of the mechanism of foot reflexology.Foot reflexology has been reported to affect hearing recovery,but no physiological evidence has been provided.This lack of evidence hampers the acceptance of the technique in clinical practice.CASE SUMMARY A girl was taken to North Sichuan Medical University Affiliated Hospital for a hearing screen by her parents.Her parents reported that her hearing level was the same as when she was born.The girl was diagnosed with sensorineural hearing loss(SNHL)by a doctor in the otolaryngology department.After we introduced the foot reflexology project,the parents agreed to participate in the experiment.After 6 months of foot reflexology treatment,the hearing threshold of the girl recovered to a normal level,below 30 dB.CONCLUSION Foot reflexology should be encouraged in clinical practice and for families of infants with SNHL.

Algorithms and statistical analysis for linear structured weighted total least squares problem

Weighted total least squares(WTLS)have been regarded as the standard tool for the errors-in-variables(EIV)model in which all the elements in the observation vector and the coefficient matrix are contaminated with random errors.However,in many geodetic applications,some elements are error-free and some random observations appear repeatedly in different positions in the augmented coefficient matrix.It is called the linear structured EIV(LSEIV)model.Two kinds of methods are proposed for the LSEIV model from functional and stochastic modifications.On the one hand,the functional part of the LSEIV model is modified into the errors-in-observations(EIO)model.On the other hand,the stochastic model is modified by applying the Moore-Penrose inverse of the cofactor matrix.The algorithms are derived through the Lagrange multipliers method and linear approximation.The estimation principles and iterative formula of the parameters are proven to be consistent.The first-order approximate variance-covariance matrix(VCM)of the parameters is also derived.A numerical example is given to compare the performances of our proposed three algorithms with the STLS approach.Afterwards,the least squares(LS),total least squares(TLS)and linear structured weighted total least squares(LSWTLS)solutions are compared and the accuracy evaluation formula is proven to be feasible and effective.Finally,the LSWTLS is applied to the field of deformation analysis,which yields a better result than the traditional LS and TLS estimations.

The Effectiveness of the Squared Error and Higgins-Tsokos Loss Functions on the Bayesian Reliability Analysis of Software Failure Times under the Power Law Process

Reliability analysis is the key to evaluate software’s quality. Since the early 1970s, the Power Law Process, among others, has been used to assess the rate of change of software reliability as time-varying function by using its intensity function. The Bayesian analysis applicability to the Power Law Process is justified using real software failure times. The choice of a loss function is an important entity of the Bayesian settings. The analytical estimate of likelihood-based Bayesian reliability estimates of the Power Law Process under the squared error and Higgins-Tsokos loss functions were obtained for different prior knowledge of its key parameter. As a result of a simulation analysis and using real data, the Bayesian reliability estimate under the Higgins-Tsokos loss function not only is robust as the Bayesian reliability estimate under the squared error loss function but also performed better, where both are superior to the maximum likelihood reliability estimate. A sensitivity analysis resulted in the Bayesian estimate of the reliability function being sensitive to the prior, whether parametric or non-parametric, and to the loss function. An interactive user interface application was additionally developed using Wolfram language to compute and visualize the Bayesian and maximum likelihood estimates of the intensity and reliability functions of the Power Law Process for a given data.

A Radial Basis Function Method with Improved Accuracy for Fourth Order Boundary Value Problems

Accurately approximating higher order derivatives is an inherently difficult problem. It is shown that a random variable shape parameter strategy can improve the accuracy of approximating higher order derivatives with Radial Basis Function methods. The method is used to solve fourth order boundary value problems. The use and location of ghost points are examined in order to enforce the extra boundary conditions that are necessary to make a fourth-order problem well posed. The use of ghost points versus solving an overdetermined linear system via least squares is studied. For a general fourth-order boundary value problem, the recommended approach is to either use one of two novel sets of ghost centers introduced here or else to use a least squares approach. When using either ghost centers or least squares, the random variable shape parameter strategy results in significantly better accuracy than when a constant shape parameter is used.

Beneficial effects of antidepressant mirtazapine in functional dyspepsia patients with weight loss

AIM: To explore the effects and mechanism of action of antidepressant mirtazapine in functional dyspepsia(FD) patients with weight loss.METHODS: Sixty depressive FD patients with weight loss were randomly divided into a mirtazapine group(MG), a paroxetine group(PG) or a conventional therapy group(CG) for an 8-wk clinical trial. Adverse effects and treatment response were recorded. The Nepean Dyspepsia Index-symptom(NDSI) checklist and the 17-item Hamilton Rating Scale of Depression(HAMD-17) were used to evaluate dyspepsia and depressive symptoms, respectively. The body composition analyzer was used to measure body weight and fat. Serum hormone levels were measured by ELISA.RESULTS:(1) After 2 wk of treatment, NDSI scores were significantly lower for the MG than for the PG and CG;(2) After 4 or 8 wk of treatment, HAMD-17 scores were significantly lower for the MG and PG than for the CG;(3) After 8 wk of treatment, patients in the MG experienced a weight gain of 3.58 ± 1.57 kg, which was significantly higher than that observed for patients in the PG and CG. Body fat increased by 2.77 ± 0.14kg, the body fat ratio rose by 4%, and the visceral fat area increased by 7.56 ± 2.25 cm2; and(4) For the MG, serum hormone levels of ghrelin, neuropeptide Y(NPY), motilin(MTL) and gastrin(GAS) were significantly upregulated; in contrast, those of leptin, 5-hydroxytryptamine(5-HT) and cholecystokinin(CCK) were significantly downregulated. CONCLUSION: Mirtazapine not only alleviates symptoms associated with dyspepsia and depression linked to FD in patients with weight loss but also significantly increases body weight(mainly the visceral fat in body fat). The likely mechanism of mirtazapine action is regulation of brain-gut or gastrointestinal hormone levels.

基于改进型Softmax Loss损失函数的面部表情识别

针对面部表情识别中Softmax Loss损失函数类内特征分散、类间特征未分离的问题,提出一种改进型Softmax Loss损失函数ACAM Loss。上述损失函数将优化集中在余弦角度上,在特征与目标权重的余弦夹角加上间隔m,在特征与非目标权重的余弦夹角减去间隔m,有效引导网络学习具备类内距离较小、类间距离较大的强区分度特征,同时加大网络训练力度优化输出,以提升分类效果。实验表明,在CK+和JAFFE数据集上,ACAM Loss函数训练模型表情识别准确率分别达到98.87%和98.92%,相比Softmax Loss函数平均增幅分别为1.35%和1.3%,识别准确率处于主流领先水平,验证了改进损失函数的有效性。

The Credibility Models under LINEX Loss Functions

LINEX(linear and exponential) loss function is a useful asymmetric loss function. The purpose of using a LINEX loss function in credibility models is to solve the problem of very high premium by suing a symmetric quadratic loss function in most of classical credibility models. The Bayes premium and the credibility premium are derived under LINEX loss function. The consistency of Bayes premium and credibility premium were also checked. Finally, the simulation was introduced to show the differences between the credibility estimator we derived and the classical one.

A STUDY ON THE WEIGHT FUNCTION OF THE MOVING LEAST SQUARE APPROXIMATION IN THE LOCAL BOUNDARY INTEGRAL EQUATION METHOD

The meshless method is a new numerical technique presented in recent years.It uses the moving least square(MLS)approximation as a shape function.The smoothness of the MLS approximation is determined by that of the basic function and of the weight function,and is mainly determined by that of the weight function.Therefore,the weight function greatly affects the accuracy of results obtained.Different kinds of weight functions,such as the spline function, the Gauss function and so on,are proposed recently by many researchers.In the present work,the features of various weight functions are illustrated through solving elasto-static problems using the local boundary integral equation method.The effect of various weight functions on the accuracy, convergence and stability of results obtained is also discussed.Examples show that the weight function proposed by Zhou Weiyuan and Gauss and the quartic spline weight function are better than the others if parameters c and α in Gauss and exponential weight functions are in the range of reasonable values,respectively,and the higher the smoothness of the weight function,the better the features of the solutions.

Application of Least Square Support Vector Machine (LSSVM) for Determination of Evaporation Losses in Reservoirs

This article adopts Least Square Support Vector Machine (LSSVM) for prediction of Evaporation Losses (EL) in reservoirs. LSSVM is firmly based on the theory of statistical learning, uses regression technique. The input of LSSVM model is Mean air temperature (T) (?C), Average wind speed (WS)(m/sec), Sunshine hours (SH)(hrs/day), and Mean relative humidity(RH)(%). LSSVM has been used to compute error barn of predicted data. An equation has been developed for the determination of EL. Sensitivity analysis has been also performed to investigate the importance of each of the input parameters. A comparative study has been presented between LSSVM and artificial neural network (ANN) models. This study shows that LSSVM is a powerful tool for determination EL in reservoirs.

Weak Type Estimates for Intrinsic Square Functions on Weighted Morrey Spaces

In this paper, we will obtain the weak type estimates of intrinsic square func- tions including the Lusin area integral, Littlewood-Paley g-function and g^-function on the weighted Morrey spaces L^1,k （w） for 0〈k〈 1 and w ∈ A1.

Empty-nest elderly:mahjong and Chinese square dance for improving the cognitive function in the community

Objective: The elderly population has proliferated worldwide. The empty-nest family pattern has become predominant among the aging people, and they are more vulnerable to the development of cognitive disorders. However, there is no standardized service in the community nursing care that includes procedures on how to improve the cognitive function of the elderly. Meanwhile, the booming number of empty-nest elderly stimulates the community nurses to assume the responsibility for their care. All of these bring more difficulties and opportunities for community nurses who are dedicated to the prevention of geriatric cognitive disorders.Methods: The authors reviewed the literature related to "empty-nest elderly", "cognitive function","mahjong",and "Chinese square dance" in the Elsevier, Web of Science(WOS), China National Knowledge Infrastructure(CNKI), Springer and PubMed databases. The study illustrates the utility possibility of an efficient and straightforward method for improving the cognitive function among the elderly in the context of community nursing care in China and even in the rest of the world.Results: Mental and physical activity contributes to cognitive fitness and may be beneficial in delaying cognitive decline. Mental activities, such as playing mahjong, and physical activities, such as the Chinese square dance, are common Chinese activities. Both of them can affect cognitive function in some way.Conclusions: China is experiencing one of its most severe aging problems. Community health personnel and related professionals may consider using mahjong and Chinese square dance to promote psychological health in empty-nest elderly individuals in the community.

Least-Square Support Vector Machine and Wavelet Selection for Hearing Loss Identification

Hearing loss(HL)is a kind of common illness,which can significantly reduce the quality of life.For example,HL often results in mishearing,misunderstanding,and communication problems.Therefore,it is necessary to provide early diagnosis and timely treatment for HL.This study investigated the advantages and disadvantages of three classical machine learning methods:multilayer perceptron(MLP),support vector machine(SVM),and least-square support vector machine(LS-SVM)approach andmade a further optimization of the LS-SVM model via wavelet entropy.The investigation illustrated that themultilayer perceptron is a shallowneural network,while the least square support vector machine uses hinge loss function and least-square optimizationmethod.Besides,a wavelet selection method was proposed,and we found db4 can achieve the best results.The experiments showed that the LS-SVM method can identify the hearing loss disease with an overall accuracy of three classes as 84.89±1.77,which is superior to SVM andMLP.The results show that the least-square support vector machine is effective in hearing loss identification.

Estimation under a Finite Mixture of Exponentiated Exponential Components Model and Balanced Square Error Loss

By exponentiating each of the components of a finite mixture of two exponential components model by a positive parameter, several shapes of hazard rate functions are obtained. Maximum likelihood and Bayes methods, based on square error loss function and objective prior, are used to obtain estimators based on balanced square error loss function for the parameters, survival and hazard rate functions of a mixture of two exponentiated exponential components model. Approximate interval estimators of the parameters of the model are obtained.

BOUNDEDNESS OF STEIN'S SQUARE FUNCTIONS ASSOCIATED TO OPERATORS ON HARDY SPACES

Let （X, d,μ） be a metric measure space endowed with a metric d and a nonnegative Borel doubling measure μ. Let L be a second order non-negative self-adjoint operator on L^2（X）. Assume that the semigroup e^-tL generated by L satisfies the Davies-Gaffney estimates. Also, assume that L satisfies Plancherel type estimate. Under these conditions, we show that Stein＇s square function Gδ（L） arising from Bochner-Riesz means associated to L is bounded from the Hardy spaces HL^p（X） to L^p（X） for all 0 〈 p ≤ 1.

Application of Weighted Cross-Entropy Loss Function in Intrusion Detection

The deep learning model is overfitted and the accuracy of the test set is reduced when the deep learning model is trained in the network intrusion detection parameters, due to the traditional loss function convergence problem. Firstly, we utilize a network model architecture combining Gelu activation function and deep neural network;Secondly, the cross-entropy loss function is improved to a weighted cross entropy loss function, and at last it is applied to intrusion detection to improve the accuracy of intrusion detection. In order to compare the effect of the experiment, the KDDcup99 data set, which is commonly used in intrusion detection, is selected as the experimental data and use accuracy, precision, recall and F1-score as evaluation parameters. The experimental results show that the model using the weighted cross-entropy loss function combined with the Gelu activation function under the deep neural network architecture improves the evaluation parameters by about 2% compared with the ordinary cross-entropy loss function model. Experiments prove that the weighted cross-entropy loss function can enhance the model’s ability to discriminate samples.

Design of Radial Basis Function Network Using Adaptive Particle Swarm Optimization and Orthogonal Least Squares

This paper presents a two-level learning method for designing an optimal Radial Basis Function Network (RBFN) using Adaptive Velocity Update Relaxation Particle Swarm Optimization algorithm (AVURPSO) and Orthogonal Least Squares algorithm (OLS) called as OLS-AVURPSO method. The novelty is to develop an AVURPSO algorithm to form the hybrid OLS-AVURPSO method for designing an optimal RBFN. The proposed method at the upper level finds the global optimum of the spread factor parameter using AVURPSO while at the lower level automatically constructs the RBFN using OLS algorithm. Simulation results confirm that the RBFN is superior to Multilayered Perceptron Network (MLPN) in terms of network size and computing time. To demonstrate the effectiveness of proposed OLS-AVURPSO in the design of RBFN, the Mackey-Glass Chaotic Time-Series as an example is modeled by both MLPN and RBFN.