Proper Rotation Research Articles

The Asymptotically Sharp Geometric Rigidity Interpolation Estimate in Thin Bi-Lipschitz Domains

This work is part of a program of development of asymptotically sharp geometric rigidity estimates for thin domains. A thin domain in three dimensional Euclidean space is roughly a small neighborhood of regular enough two dimensional compact surface. We prove an asymptotically sharp geometric rigidity interpolation inequality for thin domains with little regularity. In contrast to that celebrated Friesecke et al. rigidity estimate [Commun. Pure Appl. Math. 55(11):1461–1506, 2002] for plates, our estimate holds for any proper rotations $\boldsymbol {R}\in SO(3)$ . Namely, the estimate bounds the $L^{p}$ distance of the gradient of any $\boldsymbol {y}\in W^{1,p}(\Omega ,\mathbb{R}^{3})$ field from any constant proper rotation $\boldsymbol {R}\in SO(3)$ , in terms of the average $L^{p}$ distance (nonlinear strain) of the gradient $\nabla \boldsymbol {y}$ from the rotation group $SO(3)$ , and the average $L^{p}$ distance of the field itself from the set of rigid motions corresponding to the rotation $\boldsymbol {R}$ . There are several remarkable facts about the estimate: 1. The constants in the estimate are sharp in terms of the domain thickness scaling for any thin domains with the required regularity. 2. In the special cases when the domain has positive or negative Gaussian curvature, the inequality reduces the problem of estimating the gradient $\nabla \boldsymbol {y}$ in terms of the prototypical nonlinear strain $\int _{\Omega }\mathrm{dist}^{p}(\nabla \boldsymbol {y}(x),SO(3))dx$ to the easier problem of estimating only the vector field $\boldsymbol {y}$ in terms of the nonlinear strain without any loss in the constant scalings as the Ansatze suggest. The later will be a geometric rigidity Korn-Poincare type estimate. This passage is major progress in the thin domain rigidity problem. 3. For the borderline energy scaling (bending-to-stretching), the estimate implies improved strong compactness on the vector fields for free. Finally, this being said, our new interpolation inequality reduces the problem of proving “any” geometric one well rigidity problem in thin domains to estimating the vector field itself instead of the gradient, thus reducing the complexity of the problem.

Adaptive carrier fringe pattern enhancement for wavelet transform profilometry through modifying intrinsic time-scale decomposition.

The uneven background illumination and random noise will degrade the quality of the optical fringe pattern, resulting in reduced accuracy or errors in phase extraction of wavelet transform profilometry (WTP). An adaptive fringe pattern enhancement method is proposed in this paper, which can effectively solve the above problems and improve the robustness of WTP. First, a modified intrinsic time-scale decomposition (MITD) algorithm is used to decompose each row of the fringe pattern adaptively, which can obtain a set of reasonable and pure proper rotation components (PRCs) with a frequency ranging from high to low and a monotonic trend. The MITD algorithm can overcome the mode mixing problem while ensuring the completeness of decomposition. Then, based on the obtained pure PRCs, an innovative background-carrier signal-noise automatic grouping strategy is proposed. Specifically, weighted-permutation entropy (WPE) is adopted to handle noise removal, and fuzzy gray correlation analysis (FGCA) is used to separate the background and carrier signal. Finally, the desired phase information can be easily and accurately extracted from the enhanced carrier signal component by a direct wavelet ridge detection method. Both the simulation and experimental results demonstrate the effectiveness and functionality of the proposed method.

Direct Membrane Filtration for Wastewater Treatment Using an Intermittent Rotating Hollow Fiber Module

Direct membrane filtration of municipal wastewater has attracted a considerable interest in recent years. Preventing severe membrane fouling is a crucial issue in the process development. This paper aims to assess the effectiveness of a rotating hollow fiber module in enhancing fouling control. The effect of rotation speed, intermittence and permeate flux was studied in short-term tests at lab-scale. A combined filtration model considering residual fouling, intermediate pore blocking and cake filtration was used to analyze the effect of the shear induced by rotation. Results showed a significant flux improvement by increasing rotation shear stress and showed a nearly linear correlation between the threshold flux (ranged between 12 and 32 L·h−1·m−2) and the rotation speed. A proper rotation intermittence (10/15 on/off) was found, which may maintain a fouling control comparable to that achieved for continuous rotation. For a given energy demand, the optimal operating conditions involve high speeds (≥180 rev·min−1) with low to moderate intermittences. Analyzing the relative contribution of the different feedwater fractions on membrane fouling, colloidal particles and macromolecules were found to be the main contributors.

Longitudinal drift of Tayler instability eigenmodes as a possible explanation for super-slowly rotating Ap stars

Context. Rotation periods inferred from the magnetic variability of some Ap stars are incredibly long, exceeding ten years in some cases. An explanation for such slow rotation is lacking. Aims. This paper attempts to provide an explanation of the super-slow rotation of the magnetic and thermal patterns of Ap stars in terms of the longitudinal drift of the unstable disturbances of the kink-type (Tayler) instability of their internal magnetic field. Methods. The rates of drift and growth were computed for eigenmodes of Tayler instability using stellar parameters estimated from a structure model of an A star. The computations refer to the toroidal background magnetic field of varied strength. Results. The non-axisymmetric unstable disturbances drift in a counter-rotational direction in the co-rotating reference frame. The drift rate increases with the strength of the background field. For a field strength exceeding the (equipartition) value of equal Alfven and rotational velocities, the drift rate approaches the proper rotation rate of a star. The eigenmodes in an inertial frame show very slow rotation in this case. Patterns of magnetic and thermal disturbances of the slowly rotating eigenmodes are also computed. Conclusions. The counter-rotational drift of Tayler instability eigenmodes is a possible explanation for the observed phenomenon of super-slowly rotating Ap stars.

Our Experiences with Plastic and Reconstructive Surgery Procedures during Coronavirus Disease 2019 Pandemic.

Infection control measures, including workflow optimization, useful epidemiologic survey methods, and personal full protective clothing, were discussed. Characteristics and outcomes of emergency cases and elective cases under local and general anesthesia during the COVID-19 pandemic were summarized. A hierarchy of interventions were applied mainly from 4 aspects. First, administration control and online consultation significantly decreased patient attendance. Second, a triage workflow was established to identify high-/low-risk patients, with clinical manifestations (fever, fatigue, cough, nasal discharge, etc), epidemiologic survey, blood test, chest computed tomographic scan, and coronavirus test if necessary. Third, strict environmental control was adopted using increasing ventilation, isolated room for inpatients, etc. Fourth, proper rotation of healthcare staff was ensured to reduce workload and minimize possible contact. A total of 904 emergency interventions, 2561 local anesthesia, and 570 general anesthesia were performed during this period, and none of the cases/healthcare professionals were found to be infected. Our experience could help global plastic and reconstructive healthcare professionals to get better preparation and continue to give qualified medical services during the COVID-19 pandemic. Proper adjustments should be taken according to their own clinical settings.

‘Rotational alignment on patients’ clinical outcome of total knee arthroplasty: Distal femur axillary X-ray view to qualify rotation of the femoral component

“Background: Rotation of the femoral component in total knee replacement (TKR) is very important for good long-term results. Malrotation of the femoral component usually requires subsequent reimplantation. We performed X-ray projections of the knee at 90° to determine proper rotation of the femoral component without use of computed tomography.

An improved self-calibration method with consideration of inner lever-arm effects for a dual-axis rotational inertial navigation system

In high-precision navigation applications, a well-designed self-calibration method is a convenient approach to ensuring the positioning performance of a rotational inertial navigation system (RINS). Benefiting from the gimbal structure, traditional inertial measurement unit (IMU) sensor errors, including gyro drifts, accelerometer biases, scale factor errors and installation errors, could be estimated through a filter process under a proper rotation scheme. However, when the IMU rotates, inner lever-arm effects may bring additional errors to the observations, which may reduce the self-calibration accuracy. In this paper, an improved self-calibration method that includes consideration of the inner lever-arm effect is proposed for a dual-axis RINS. Based on analysis of the error propagation characteristics, a novel rotation scheme with variable angular rate is designed. By adopting the proposed self-calibration method, traditional IMU sensor errors can achieve much higher accuracy, and the inner lever-arm parameters can also be well calibrated simultaneously. Long-term vehicle navigation indicates that the positioning accuracy was significantly enhanced after the compensation of the calibration results, fully illustrating the effectiveness of the proposed method in ameliorating navigation performance for the dual-axis RINS.

New Expansion Rate Estimate of the Scorpius–Centaurus Association Based on T Tauri Stars from the Gaia DR2 Catalog

The kinematic properties of the Scorpius-Centaurus association were studied using spatial velocities of approximately 700 young T Tauri stars. Their proper motions and trigonometric parallaxes were selected by Zari et al. from the Gaia DR2 catalog, and radial velocities were taken from various sources. The linear expansion coefficients new estimate of the association $K=39\pm2$ km/s/kpc is obtained by considering the influence of the galactic spiral density wave with an amplitude of radial disturbances $f_R=5$ km/s and solar phase in the wave $-120^\circ$. The proper rotation of the association is shown to be small. The residual velocity ellipsoid of these stars has semimajor axes $\sigma_{1,2,3}=(7.72,1.87,1.74)\pm(0.56,0.37,0.22)$ km/s and is positioned at an angle $12\pm2^\circ$ to the galactic plane.

Injection practices among the diabetes patients on insulin therapy in Chitradurga

Background: Diabetes mellitus has emerged as a global health problem. Insulin is the essential treatment modality for significant number of diabetic patients. Nearly 4 out of 10 patients with T2DM in India are using insulin, either alone or in combination with oral hypoglycemic drugs (OADs). The present study was conducted with the objectives to assess the knowledge and insulin injection practices, risk factors of sharps injury among diabetic patients who are on insulin treatment.Methods: Cross sectional study was conducted at a Basaveshwara hospital, Chitradurga, among diabetic patients on insulin treatment for minimum of past 6 months. Insulin injection technique questionnaire (ITQ) was administered to study participants and proforma was filled by interview technique.Results: 100 patients participated in the study. Syringe and needle device and 6 mm needle are most commonly used devices. 5-10 seconds was the most common duration of time (54%) for which the patients continued to keep the needle inserted in injection site after insulin injection. A 26% patients had lip hypertrophy at injection sites. Among households of the insulin users, a high percentage of children (41%) and house keeper/waste collectors (32%) were at risk of sharps injury. A majority of 55% of the insulin users reported that they didn’t have appropriate disposal containers for the used sharps.Conclusions: A 29% of participants did not practice proper rotation of injection site. Reuse of insulin needles was common practice. Pain/tenderness, lipo-hypertrophy and leakage of insulin after injection were noted at injection site. 10 seconds dwell time was reportedly practiced by only 23%, about half of patients skipped insulin injections and didn’t follow appropriate disposal of used sharps, thereby exposing both family members and waste collectors/handlers to higher risks of sharps injuries.

Analytical solutions of electroelastic fields in piezoelectric thin-film multilayer: applications to piezoelectric sensors and actuators

Analytical expressions are derived for the stresses and electric fields induced in a piezoelectric multilayer deposited on a substrate with lattice misfit and thermal expansion coefficient mismatch. The piezoelectric multilayer can be subjected to an externally applied force, moment and electric potential. The derived formulations can model any number of layers using recursive relations that minimize the computation time. A proper rotation matrix is utilized to generalize the derived expressions to accommodate various orientations allowing each layer to have hexagonal crystal symmetry. The influence of lattice misfit and thermal expansion coefficient mismatch in the presence of externally applied force, moment and electric potential on the state of the electroelastic fields in each layer is evaluated for various applications. Comparison with finite element analysis results shows excellent agreement. The analytical expressions developed here can be useful in designing electromechanical sensors, actuators and optoelectronic devices made from piezoelectric multilayers.

The importance of crop rotation in intensive vegetable production in a greenhouse

Intensive vegetable production nowadays is a large ?energy consumer?. Producers look only at the economic side of such production, which is most often the excuse for its current realization. Intensive vegetable production is now reduced to cultivating several vegetable species, more often in the single-crop system. By thinking this way we bring the entire production into an unsustainable situation. Therefore, vegetable production in a greenhouse should be organized by growing preceding crops, main crops and stubble crops. Intensive vegetable production implies the maximally well-organized use of available land and resources. This includes proper crop rotation and cultivation practices, as well as knowledge of the market, as a possibility of placing excess production. With the good organization of crop rotation and the timely replacement of crops, we can transform a greenhouse into a useful place for vegetable production.

On the subject of influence of dissipative and permanent momentums on stability of uniform rotations of two elastically connected free gyroscopes of Lagrange

In many works, there are studies of the asymptotic stability of rotation of a free Lagrange gyroscope in a resisting medium. This article generalizes this problem to the case of uniform rotations of two free Lagrange gyroscopes connected by an elastic restoring spherical hinge. The rotation of each gyroscope is maintained by a constant moment in an inertial coordinate system. The characteristic equation of the perturbed motion is presented in the form of an algebraic equation of the fourth degree with complex coefficients. Based on the innor approach, conditions of asymptotic stability are obtained in the form of a system of three inequalities. The left-hand side of these inequalities is represented, respectively, in the form of determinants of the third, fifth, and seventh orders. Up to first-order values of smallness, relative to the reciprocal of the stiffness coefficient, a study is made of the effect of the joint stiffness on stability conditions. From the conditions of positivity of the highest coefficients in three inequalities, it is shown that for a sufficiently large rigidity, the stability conditions are determined by only one inequality. Cases of degeneration of an elastic spherical joint into a spherical inelastic, cylindrical, and universal elastic joint (Hooke's joint) are considered. In the case of an inelastic spherical joint, the system of three inequalities is slightly simplified. The greatest simplification arises in the case of a cylindrical hinge. In this case, the characteristic equation is represented as a quadratic equation with complex coefficients. According to the innoric approach, the conditions of asymptotic stability are written in the form of a single inequality, the left side of which is presented in the form of third-order determinants. It is shown that this inequality coincides with the inequality obtained earlier for the case of a sufficiently large rigidity of the hinge. If the angular velocities of the proper rotations of the gyroscopes coincide, the inequality obtained for the cylindrical hinge coincides with the well-known inequality for one gyroscope. In the case of a universal elastic hinge (Hooke's hinge), the first inequality is represented as a square inequality with respect to the angular velocity of proper rotation.

Analysis of SamSat-218D nanosatelite motion acording to trajectory measurements

The motion of the SamSat-218D nanosatellite is analyzed by trajectory measurements. Special features of nanosatellite behavior in low orbits were experimentally confirmed. These features are due to both the influence of the atmosphere and the nanosatellites inherent mass-inertia characteristics: the orbital lifetime of nanosatellites is shorter, whereas angular acceleration generated by the aerodynamic moment couple is much higher than that of satellites with large sizes and masses. Variation of the ballistic coefficient in time is estimated from known trajectory measurements and information on the average density of the atmosphere at the points of trajectory measurements. The ballistic coefficient of the SamSat-218D nanosatellite having the shape of a rectangular parallelepiped depends on the spatial angle of attack and the angle of proper rotation. The ratio of the maximum value of the ballistic coefficient to the minimum value is 4.75. This made it possible to evaluate the nature of possible motion relative to the nanosatellite center of mass by the behavior of the ballistic coefficient. The most probable motion relative to the center of mass of the SamSat-218D nanosatellite is the transient motion between different equilibrium positions, due to commensurate aerodynamic and gravitational moments and insignificant angular velocities.

An intrinsic timescale decomposition-based kernel extreme learning machine method to detect tool wear conditions in the milling process

Detecting tool wear conditions in milling process is of significance to enhance the reliability of machining equipment. However, traditional methods have run into difficulties due to interference from strong noise and other unknown vibration sources. To solve this problem, an intrinsic timescale decomposition (ITD) technique is combined with a kernel extreme learning machine (KELM) technique. In this method, ITD is firstly employed to decompose multiple sensor signals into several sets of proper rotation (PR) components. Next, the optimal PR component of each set is selected by correlation coefficient analysis. A series of feature sets are then constructed according to the data indicators extracted from the selected PR components in time and frequency domains. Finally, the feature sets are fed into the KELM, which classifies the tool wear conditions. Experimental investigations are conducted to determine three stages of tool wear in the milling process; the ITD-KELM method achieved 93.28% classification accuracy, which verifies its feasibility and effectiveness for detecting tool wear. The superior performance of the proposed method is further demonstrated by comparing it with four other methods: ITD-based SVM, EEMD-based KELM, VMD-based KELM, and KELM.

New cases of regular precession of an asymmetric liquid-filled rigid body

To describe the rotation of a rigid body with an ellipsoidal cavity filled with an ideal vorticated liquid, the Poincare–Hough–Zhukovsky equations are used. It is obtained constraints (hereinafter referred to as configuration conditions) on the mass distribution and cavity dimensions of an asymmetric liquid-filled rigid body under which the rigid body can perform the regular precession. Two possible nontrivial cases are indicated when one or two components of the direct vector of the axis of proper rotation are equal to zero. It is shown that if the axis of proper rotation coincides with one of the principal axes of inertia of the system, it suffices to fulfill one configuration condition. The ratio between the periods of proper rotation and precession is found. The regular precession of a system in which the principal moments of inertia are close to each other and the cavity is close to spherical is considered. For the case when the difference between the two equatorial moments of inertia is an order of magnitude smaller than the difference between the equatorial and polar moments, the main part of the ratio between the periods coincides with the Euler period, and the correction is due to the inequality between the equatorial moments of inertia. In the case when the axis of proper rotation is orthogonal to a principal axis and is not coincident with any other principal axis, the number of configuration conditions is two. Expressions for the rates of precession and proper rotation are obtained, and the position in the body of the axis of proper rotation is indicated. Special cases are given that allow simplification of the configuration conditions.

Rotating Machinery Fault Diagnosis Based on Improved Multiscale Amplitude-Aware Permutation Entropy and Multiclass Relevance Vector Machine.

The health state of rotating machinery directly affects the overall performance of the mechanical system. The monitoring of the operation condition is very important to reduce the downtime and improve the production efficiency. This paper presents a novel rotating machinery fault diagnosis method based on the improved multiscale amplitude-aware permutation entropy (IMAAPE) and the multiclass relevance vector machine (mRVM) to provide the necessary information for maintenance decisions. Once the fault occurs, the vibration amplitude and frequency of rotating machinery obviously changes and therefore, the vibration signal contains a considerable amount of fault information. In order to effectively extract the fault features from the vibration signals, the intrinsic time-scale decomposition (ITD) was used to highlight the fault characteristics of the vibration signal by extracting the optimum proper rotation (PR) component. Subsequently, the IMAAPE was utilized to realize the fault feature extraction from the PR component. In the IMAAPE algorithm, the coarse-graining procedures in the multi-scale analysis were improved and the stability of fault feature extraction was promoted. The coarse-grained time series of vibration signals at different time scales were firstly obtained, and the sensitivity of the amplitude-aware permutation entropy (AAPE) to signal amplitude and frequency was adopted to realize the fault feature extraction of coarse-grained time series. The multi-classifier based on the mRVM was established by the fault feature set to identify the fault type and analyze the fault severity of rotating machinery. In order to demonstrate the effectiveness and feasibility of the proposed method, the experimental datasets of the rolling bearing and gearbox were used to verify the proposed fault diagnosis method respectively. The experimental results show that the proposed method can be applied to the fault type identification and the fault severity analysis of rotating machinery with high accuracy.

A Specific Sugar Moiety in the Lactococcus lactis Cell Wall Pellicle Is Required for Infection by CHPC971, a Member of the Rare 1706 Phage Species.

Lactococcus lactis is a Gram-positive bacterium widely used as a starter culture for the production of different dairy products, especially a large variety of cheeses. Infection of lactococcal starter cultures by bacteriophages is one of the major causes of fermentation failure and often leads to production halt. Lactococcal bacteriophages belonging to the c2, 936, and P335 species are the most commonly isolated in dairy plants and have been extensively investigated in the past three decades. Information regarding bacteriophages belonging to less commonly isolated species is, on the other hand, less extensive, although these phages can also contribute to starter culture infection. Here, we report the nucleotide sequence of the newly isolated L. lactis phage CHPC971, belonging to the rare 1706 species of lactococcal phages. We investigated the nature of the host receptor recognized by the phage and collected evidence that strongly suggests that it binds to a specific sugar moiety in the cell wall pellicle of its host. An in silico analysis of the genome of phage CHPC971 identified the hypothetical genes involved in receptor binding.IMPORTANCE Gathering information on how lactococcal bacteriophages recognize their host and proliferate in the dairy environment is of vital importance for the establishment of proper starter culture rotation plans and to avoid fermentation failure and consequent great economic losses for dairy industries. We provide strong evidence on the type of receptor recognized by a newly isolated 1706-type lactococcal bacteriophage, increasing knowledge of phage-host interactions relevant to dairying. This information can help to prevent phage infection events that, so far, are hard to predict and avoid.

Detecting the presence of anterior cruciate ligament deficiency based on a double pendulum model, intrinsic time-scale decomposition (ITD) and neural networks

The anterior cruciate ligament (ACL) possesses the function of stabilizing the knee joint through limiting anterior tibial translation and controlling tibial rotation. Patients with unilateral ACL deficiency often demonstrate alterations of knee kinematics, kinetics and gait patterns in the deficient side in comparison to the unaffected contralateral side. This also leads to the early onset of osteoarthritis. In order to detect and monitor the progression of ACL deficiency over time, various classification approaches using spatiotemporal gait variables have been presented. In this study we propose a novel method for classifying gait patterns between ACL-deficient (ACLD) knee and unaffected contralateral ACL-intact (ACLI) knee based upon gait system dynamics, intrinsic time-scale decomposition (ITD) and neural networks. First, human leg is modeled as a double-pendulum to imitate and simplify the human walking. Since the lower extremities act as a kinetic chain during dynamic tasks, control of the hip joint will interact with knee motion. Related gait kinematic parameters including knee and hip joint angle and angular velocity are decomposed into a series of proper rotation components (PRCs) and a baseline signal by using the ITD method. The first PRCs of knee and hip joint angle and angular velocity are extracted, which contain most of the kinematic signals’ vibration energy and are considered to be the predominant PRCs. Third, neural networks are then used as the classifier with feature vectors as the input to distinguish between ACLD and ACLI knees based on the difference of gait system dynamics between the two groups. Finally, experiments are carried out on forty-three patients to assess the effectiveness of the proposed method. By using the leave-one-out cross-validation style under normal and fast walking speed conditions, the correct classification rates are reported to be $$95.12\%$$ and $$93.28\%$$, respectively. In comparison to other state-of-the-art methods, the results demonstrate superior performance and the proposed method may serve as a potential assistant tool for the automatic detection of ACL deficiency in the clinical application.

A strategy combining intrinsic time-scale decomposition and a feedforward neural network for automatic seizure detection

Epilepsy is a common neurological disorder which can occur in people of all ages globally. For the clinical treatment of epileptic patients, the detection of epileptic seizures is of great significance. Objective: Electroencephalography (EEG) is an essential component in the diagnosis of epileptic seizures, from which brain surgeons can detect important pathological information about patient epileptiform discharges. This paper focuses on adaptive seizure detection from EEG recordings. We propose a new feature extraction model based on an adaptive decomposition method, named intrinsic time-scale decomposition (ITD), which is suitable for analyzing non-linear and non-stationary data. Approach: Firstly, using the ITD technique, every EEG recording is decomposed into several proper rotation components (PRCs). Secondly, the instantaneous amplitudes and frequencies of these PRCs can be calculated and then we extract their statistical indices. Furthermore, we combine all these statistical indices of the corresponding five PRCs as the feature vector of each EEG signal. Finally, these feature vectors are fed into a feedforward neural network (FNN) classifier for EEG classification. The whole process of feature extraction proposed in this paper only involves one parameter and the role of the ITD method is based on a piecewise linear function, which makes the computation of the model simple and fast. More useful information for classification can be obtained since we take advantage of both instantaneous amplitude and instantaneous frequency for feature extraction. Main results: We consider the 17 classification problems which contain normal versus epileptic, non-seizure versus seizure and normal versus interictal versus ictal using a FNN classifier which only contains one hidden layer. Experimental results show that the proposed method can catch the discriminative features of EEG signals and obtain comparable results when compared with state-of-the-art detection methods. Significance: Therefore, the proposed system has a great potential in real-time seizure detection and provides physicians with a real-time diagnostic aid in their practice.

Identification of epileptic seizures in EEG signals using time-scale decomposition (ITD), discrete wavelet transform (DWT), phase space reconstruction (PSR) and neural networks

Traditionally, detection of epileptic seizures based on the visual inspection of neurologists is tedious, laborious and subjective. To overcome such disadvantages, numerous seizure detection techniques involving signal processing and machine learning tools have been developed. However, there still remain the problems of automatic detection with high efficiency and accuracy in distinguishing normal, interictal and ictal electroencephalogram (EEG) signals. In this study we propose a novel method for automatic identification of epileptic seizures in singe-channel EEG signals based upon time-scale decomposition (ITD), discrete wavelet transform (DWT), phase space reconstruction (PSR) and neural networks. First, EEG signals are decomposed into a series of proper rotation components (PRCs) and a baseline signal by using the ITD method. The first two PRCs of the EEG signals are extracted, which contain most of the EEG signals’ energy and are considered to be the predominant PRCs. Second, four levels DWT is employed to decompose the predominant PRCs into different frequency bands, in which third-order Daubechies (db3) wavelet function is selected for analysis. Third, phase space of the PRCs is reconstructed based on db3, in which the properties associated with the nonlinear EEG system dynamics are preserved. Three-dimensional (3D) PSR together with Euclidean distance (ED) has been utilized to derive features, which demonstrate significant difference in EEG system dynamics between normal, interictal and ictal EEG signals. Fourth, neural networks are then used to model, identify and classify EEG system dynamics between normal (healthy), interictal and ictal EEG signals. Finally, experiments are carried out on the University of Bonn’s widely used and publicly available epilepsy dataset to assess the effectiveness of the proposed method. By using the 10-fold cross-validation style, the achieved average classification accuracy for eleven cases is reported to be 98.15%. Compared with many state-of-the-art methods, the results demonstrate superior performance and the proposed method can serve as a potential candidate for the automatic detection of seizure EEG signals in the clinical application.