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COMPUTER OPTICS 2023年第5期47卷 816-823页

作者： Galyntich, A. N. Raifeld, M. A. Branch JSC PO UOMZ Ural SibNIIRS St D Kovalchuk 179-2 Novosibirsk 630082 Russia Novosibirsk State Tech Univ K Marksa Ave 20 Novosibirsk 630073 Russia

When there is no information about the number of brightness classes, synthesizing algorithms for automatic image threshold segmentation involves a problem of determining the number of thresholds. The solution to the problem of estimating the number of classes in an image can be based on representing its distribution as a mixture of distributions of brightness classes when priori probabilities are unknown, or estimating the number of histogram modes. At the same time, it is known that the mixture splitting problem has a solution only for certain types of distributions and the histogram modes are not always distinguishable. In the general case, when the distributions of brightness classes are unknown, there are difficulties in applying these methods. The article proposes a non-parametric approach to determining the number of classes that differ in average brightness, based on rank histograms and using the property of local spatial grouping of elements of each brightness class in the image.

关键词： image segmentation nonparametric algorithm rank histogram eigenvalues Gram-Schmidt orthogonalization principal component method

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nonparametric Learning algorithms for Joint Pricing and Inventory Control with Lost Sales and Censored Demand

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MATHEMATICS OF OPERATIONS RESEARCH 2021年第2期46卷 726-756页

作者： Chen, Boxiao Chao, Xiuli Shi, Cong Univ Illinois Coll Business Adm Chicago IL 60607 USA Univ Michigan Ind & Operat Engn Ann Arbor MI 48109 USA

We consider a joint pricing and inventory control problem in which the customer's response to selling price and the demand distribution are not known a priori. Unsatisfied demand is lost and unobserved, and the only available information for decision making is the observed sales data (also known as censored demand). Conventional approaches, such as stochastic approximation, online convex optimization, and continuum-armed bandit algorithms, cannot be employed, because neither the realized values of the profit function nor its derivatives are known. A major challenge of this problem lies in that the estimated profit function constructed from observed sales data is multimodal in price. We develop a nonparametric spline approximation-based learning algorithm. The algorithm separates the planning horizon into a disjoint exploration phase and an exploitation phase. During the exploration phase, a spline approximation of the demand-price function is constructed based on sales data, and then the corresponding surrogate optimization problem is solved on a sparse grid to obtain a pair of recommended price and target inventory level. During the exploitation phase, the algorithm implements the recommended strategies. We establish a (nearly) square-root regret rate, which (almost) matches the theoretical lower bound.

关键词： nonparametric algorithm joint pricing and inventory control lost sales censored demand

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Comparing the performance of machine learning algorithms for estimating aboveground biomass in typical steppe of northern China using Sentinel imageries

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ECOLOGICAL INDICATORS 2023年第1期154卷

作者： Wu, Nitu Crusiol, Lufs Guilherme Teixeira Liu, Guixiang Wuyun, Deji Han, Guodong Inner Mongolia Agr Univ Coll Grassland Resources & Environm Key Lab Grassland Resources Minist Educ Hohhot 010011 Peoples R China Embrapa Soja Brazilian Agr Res Corp Natl Soybean Res Ctr BR-86001970 Londrina PR Brazil Chinese Acad Agr Sci Inst Grassland Res Hohhot 010010 Peoples R China Chinese Acad Agr Sci Inst Agr Resources & Reg Planning Beijing 100081 Peoples R China

Monitoring aboveground biomass (AGB) is crucial for assessing, managing, and utilizing grassland ecosystems. While the technical form of combining remote sensing and machine learning algorithms is widely used to estimate AGB at a regional scale, few studies have assessed and compared the performance of popular algorithms on the typical steppe in northern China. In this study, the northern Xilinhot, a representative area of typical steppe in China, was selected as the study area to compare the performance of six widely used machine learning algorithms for AGB estimation, namely stepwise linear regression (SLR), partial least square regression (PLS), principal component regression (PCR), random forest (RF), support vector machines (SVM), and k-nearest neighbors (KNN). Additionally, the study explored the modeling capability of multisource variables from Sentinel imagery and auxiliary data. The results showed that (1) considering the aspects of prediction accuracy, noise resistance, ease of operation, and transferability, the SLR algorithm is more suitable for estimating typical steppe AGB in northern China at the Sentinel scale. (2) Vegetation Indices (VI) play a significant role in the development of selected models, with significant contributions from both traditional and soil-adjusted indices. (3) Sentinel C-band synthetic aperture radar (SAR) is unsuitable for modeling typical steppe AGB. (4) Among the selected environmental factors, only clay content and soil pH are significantly linearly correlated with AGB, while elevation, precipitation, temperature, soil pH, and sand content are advantageous for RF prediction. This study can provide important technical references for the research on AGB in typical steppe in northern China.

关键词： Remote sensing Grassland ecosystem nonparametric algorithm Vegetation modelling

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A New Automatic Identification Method of Heart Failure Using Improved Support Vector Machine Based on Duality Optimization Technique

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IEEE ACCESS 2019年 7卷 149595-149611页

作者： Geweid, Gamal G. N. Abdallah, Mahmoud A. Univ North Dakota Sch Elect Engn & Comp Sci Elect Engn Dept Grand Forks ND 58202 USA Benha Univ Benha Fac Engn Elect Engn Dept Banha 13511 Egypt Cent State Univ Mfg Engn Dept Wilberforce OH 45384 USA

Currently, Heart failure disease is considered a multifaceted clinical disease affecting millions of people worldwide. Hospitals and cardiac centers rely heavily on ECG as a regular tool for evaluating and diagnosing Heart failure disease as an initial stage. The process of Heart failure disease identification from the ECG signal aims to reduce the time of the diagnostic process for patients with heart failure and to improve the outcomes of the detection process applied to these patients. The information acquired from the ECG signal simplifies the laboratory evaluation and other traditional diagnosis methods to evaluate and diagnose Heart failure disease. Unluckily, the problem of segmentation of the ECG signal is complicated because of similarities in time interval and amplitude between several ECG signal as well as the presence of noise in ECG signals. Most cardiologists use the ECG signal to identify Heart failure disease and their decision depends on the identification process, to determine whether surgery or medical treatment is required. This paper offers a new identification technique to overcome the current problems, such as overlapping in heart rate duration from the time interval from one PQRST wave to the next. In this study, the aim is to develop a new automatic method using improved support vector machine to diagnosis HFD from ECG signals. This is particularly relevant to ECG signals for the diagnosis of HFD as the first step to treatment and care of patients in general and specifically those with early heart disease to increase their overall survival. This paper outlines a hybrid approach of dual SVM and nonparametric algorithm to spot HFD in ECG signals leading to increase reliability and accuracy of identification and diagnosis of heart failure classes in the early stages using the proposed algorithm. The nonparametric algorithm is used to train SVM and its dual to get two models of SVM. The dual problem gives a different view that is better and some

关键词： Electrocardiography Heart Support vector machines Feature extraction Optimization Cardiac disease Heart failure disease (HFD) ECG support vector machine (SVM) nonparametric algorithm duality

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Synthesis of arbitrary-shaped textures constrained by the structure tensor field

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SIGNAL IMAGE AND VIDEO PROCESSING 2018年第1期12卷 41-49页

作者： Akl, Adib Yaacoub, Charles Donias, Marc Da Costa, Jean-Pierre Germain, Christian Holy Spirit Univ Kaslik USEK Fac Engn POB 446 Jounieh Lebanon Univ Bordeaux UMR CNRS 5218 IMS Lab Talence France

In this paper, a new algorithm is presented for the nonparametric synthesis of arbitrary-shaped textures from an initial texture sample, called the exemplar, and a reference orientation map, called the reference and intended to constrain the coarse scale orientation flow of the output texture. The synthesis process consists of three stages. First, a dictionary is constructed, composed of rotated versions of the exemplar and of their structure tensor fields. Then a synthetic structure tensor field is constructed from the exemplar's one, under the constraint of the reference. Finally, the output texture is synthesized from the previously generated structure tensor field, again under the constraint of the reference. Synthesis experiments show that the proposed method is able to faithfully reproduce the visual aspect of input samples while respecting the large-scale orientation flow of the reference. This paves the way toward the synthesis of 3-D textures of arbitrary shapes from 2-D exemplars, with applications in virtual material design.

关键词： Texture synthesis Structure tensor Orientation flow nonparametric algorithm Dictionary

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Modified Shift algorithm for Processing Measured Data

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MEASUREMENT TECHNIQUES 2015年第11期57卷 1287-1294页

作者： Solodkiy, D. A. Kistovich, A. V. All Russia Res Inst Physicotech & Radio Measureme Mendeleyevsk Russia

We develop an algorithm for separating useful signals from background noise enabling the acquisition of necessary data with a signal-to-noise ratio up to -15 dB. The efficiency of the algorithm has been tested on a nu... 详细信息

关键词： signal-to-noise ratio optical fiber nonparametric algorithm

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Discrimination between the Move and Stop of a Lift Based on Accelerometer Signals

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OPTOELECTRONICS INSTRUMENTATION AND DATA PROCESSING 2015年第2期51卷 184-191页

作者： Rajfeld, M. A. Novosibirsk State Tech Univ Pr Karla Marksa 20 Novosibirsk 630073 Russia

A solution to the problem of reliable discrimination between the move and stop states of a mine lift is proposed based on processing signals of an accelerometer mounted in the lift cabin. The accelerometer signal is processed using a combination of adaptive whitening algorithms and a nonparametric Neyman Pearson test that provides an effective solution of the problem under a priori nonparametric uncertainty conditions.

关键词： accelerometer whitening filter Neyman-Pearson test nonparametric algorithm rank statistics

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Increasing the Power of nonparametric Tests of Scale Based on Grouping Data Using Order Statistics

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OPTOELECTRONICS INSTRUMENTATION AND DATA PROCESSING 2012年第5期48卷 529-535页

作者： Raifel'd, M. A. Novosibirsk State Tech Univ Pr Karla Marksa 20 Novosibirsk 630092 Russia

An approach is proposed to increase the efficiency of nonparametric two-sample tests of scale by using a preliminarily grouping of initial observations by the level. The groups are formed using order statistics..

关键词： rank statistics nonparametric algorithm sufficient statistics variational series sample distribution function order statistics

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Estimation of Joint Impedance Using Short Data Segments

Estimation of Joint Impedance Using Short Data Segments

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33rd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS)

作者： Ludvig, Daniel Perreault, Eric J. Rehabil Inst Chicago Sensory Motor Performance Program Chicago IL 60657 USA Northwestern Univ Evanston IL 60208 USA

ISBN: (纸本)9781424441228

Joint impedance is an important property of the human muscular system and plays a role in the control of movement and posture. Previous studies showed that joint impedance varies with the position of the joint and activation level of the surrounding muscles;however, it remains unknown how it varies during movement. Non-parametric algorithms that estimate time-varying impedance do exist;however these algorithms require hundreds of realizations of the same time-varying behavior. In this paper we develop a non-parametric algorithm that can estimate slowly time-varying impedance using multiple short data segments. Using simulated data we evaluate the desired data segment length and the number of realizations needed to yield accurate estimates.

关键词： Computational modeling Correlation Data models Estimation Impedance Joints Time varying systems bioelectric potentials biomechanics human muscular system joint impedance estimation movement control muscle muscle activation level nonparametric algorithm po

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A nonparametric Adaptive CUSUM Method and Its Application in Source-End Defense against SYN Flooding Attacks

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Wuhan University Journal of Natural Sciences 2011年第5期16卷 414-418页

作者： YU Ming School of Information and Communication Engineering Dalian University of Technology Dalian 116024 Liaoning China

Combating DDoS attacks at their sources is still in its infancy. In tttis paper, a noaparametric adaptive CUSUM （cumulative sum） method is presented, which is proven efficient in detecting SYN flooding attacks close to their sources. Different from other CUSUM methods, this new method has two distinct features：（1） its detection threshold can adapt itself to various traffic conditions and （2） it can timely detect the end of an attack within a required delay. Trace-driven simulations are conducted to validate the efficacy of this method in detecting SYN flooding attacks, and the results show that the nonparametric adaptive CUSUM method excels in detecting low-rate attacks.

关键词： adaptive detection nonparametric algorithm CUSUM （cumulative sum） SYN flooding DDoS

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