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Currently, multi-layer deep convolutional networks are mostly used for field weed recognition to extract and identify target features. However, in practical application scenarios, they still face challenges such as insufficient recognition accuracy, a large number of model parameters, and slow detection speed. In response to the above problems, using chickweed as the identification object, a weed identification model based on improved YOLOv5s was proposed. Firstly, the Squeeze-and-Excitation Module (SE) and Convolutional Block Attention Module (CBAM) were added to the model’s feature extraction network to improve the model’s recognition accuracy; secondly, the Ghost convolution lightweight feature fusion network was introduced to effectively identify the volume, parameter amount, and calculation amount of the model, and make the model lightweight; finally, we replaced the loss function in the original target bounding box with the Efficient Intersection over Union (EloU) loss function to further improve the detection performance of the improved YOLOv5s model. After testing, the accuracy of the improved YOLOv5s model was 96.80%, the recall rate was 94.00%, the average precision was 93.20%, and the frame rate was 14.01 fps, which were improved by 6.6%, 4.4%, 1.0%, and 6.1%, respectively, compared to the original YOLOv5s model. The model volume was 9.6 MB, the calculation amount was 13.6 GB, and the parameter amount was 5.9 MB, which decreased by 29.4%, 14.5%, and 13.2% compared with the original YOLOv5s model, respectively. This model can effectively distinguish chickweed between crops. This research can provide theoretical and technical support for efficient identification of weeds in complex field environments.

To explore the deep geological structure of the Dongyang area in Fujian, China, gravity data from the area and its surroundings were collected and processed. Additionally, a high-precision magnetic survey was conducted in the Zhongxian region of this area, with subsequent analysis of the magnetic anomalies. Through the integration of regional geological data, a comprehensive analysis was carried out on the characteristics of gravity–magnetic anomalies and deep geological structures in the Dongyang area. The study indicates that the primary portion of the Dongyang area lies southwest of the expansive circular volcanic structure spanning Dehua to Yongtai. Two significant residual gravity anomalies were identified within the region, interpreted as the Xiaoban-Shuangqishan and Dongyang-Lingtouping residual gravity-positive anomalies. In the Zhongxian region, the magnetic field exhibits complexity with notable amplitude variations. Positive anomalies predominate in the western and northern sectors, while localized positive anomalies are prominent in the eastern region. The central area portrays a circular and disordered mix of positive and negative anomalies. Particularly distinctive are the band-shaped and fan-shaped negative anomalies curving from northeast to southeast through the central region. Various positive and negative anomalies of varying strengths, gradients, and orientations overlay both positive and negative magnetic backgrounds in specific locales. Moreover, the Dongyang area showcases well-developed fault structures, primarily oriented in northeast and northwest directions. Leveraging the regional magnetic attributes in conjunction with regional geological data, 39 faults were deduced in the Zhongxian region of the Dongyang area, delineating three promising mineralization zones.

With the development of precision sensing instruments and data storage devices, the fusion of multi-sensor data in gearbox fault diagnosis has attracted much attention. However, existing methods have difficulty in capturing the local temporal dependencies of multi-sensor monitoring information, and the inescapable noise severely decreases the accuracy of multi-sensor information fusion diagnosis. To address these issues, this paper proposes a fault diagnosis method based on dynamic graph convolutional neural networks and hard threshold denoising. Firstly, considering that the relationships between monitoring data from different sensors change over time, a dynamic graph structure is adopted to model the temporal dependencies of multi-sensor data, and, further, a graph convolutional neural network is constructed to achieve the interaction and feature extraction of temporal information from multi-sensor data. Secondly, to avoid the influence of noise in practical engineering, a hard threshold denoising strategy is designed, and a learnable hard threshold denoising layer is embedded into the graph neural network. Experimental fault datasets from two typical gearbox fault test benches under environmental noise are used to verify the effectiveness of the proposed method in gearbox fault diagnosis. The experimental results show that the proposed DDGCN method achieves an average diagnostic accuracy of up to 99.7% under different levels of environmental noise, demonstrating good noise resistance.

The automatic segmentation of cardiac computed tomography (CT) and magnetic resonance imaging (MRI) plays a pivotal role in the prevention and treatment of cardiovascular diseases. In this study, we propose an efficient network based on the multi-scale, multi-head self-attention (MSMHSA) mechanism. The incorporation of this mechanism enables us to achieve larger receptive fields, facilitating the accurate segmentation of whole heart structures in both CT and MRI images. Within this network, features extracted from the shallow feature extraction network undergo a MHSA mechanism that closely aligns with human vision, resulting in the extraction of contextual semantic information more comprehensively and accurately. To improve the precision of cardiac substructure segmentation across varying sizes, our proposed method introduces three MHSA networks at distinct scales. This approach allows for fine-tuning the accuracy of micro-object segmentation by adapting the size of the segmented images. The efficacy of our method is rigorously validated on the Multi-Modality Whole Heart Segmentation (MM-WHS) Challenge 2017 dataset, demonstrating competitive results and the accurate segmentation of seven cardiac substructures in both cardiac CT and MRI images. Through comparative experiments with advanced transformer-based models, our study provides compelling evidence that despite the remarkable achievements of transformer-based models, the fusion of CNN models and self-attention remains a simple yet highly effective approach for dual-modality whole heart segmentation.

Field emission is an important work mode for electron sources, and carbon nanotubes (CNTs) have been extensively studied for their good emission properties. It is well known that the parameters of the grid deeply influence the field emission performance of CNTs, a relationship that requires further elucidation. Therefore, in this study, the relationship between the grid aperture ratio and electron transmittance was studied through simulations and experiments. This study’s results indicate that the electron transmittance improved as the grid aperture ratio increased. Meanwhile, electron beam spot simulations and imaging experiments indicate that an increased grid aperture ratio will expand the cathode electron divergence, leading to a larger electron beam spot size. These results demonstrate that there is a trade-off in maintaining the grid aperture ratio between high electron transmittance and relatively small electron beam spot size, and the optimum grid aperture ratio is between 75% and 85%. These results will provide a reference for the design and optimization of X-ray tubes and other electron sources.

The cart–inverted pendulum system (CIPS) is a typical example of underactuated mechanical systems. For the CIPS with friction and disturbances, a gain-scheduled model predictive control method is proposed to achieve the upright stabilization objective of the single inverted pendulum (SIP) while controlling the cart to reach a desired new position. To this end, first, a dynamic equation of the CIPS with friction and disturbances is formulated based on the Newton–Euler equation. On the basis of the dynamic equation of the CIPS, its motion characteristics and control process are analyzed. Next, the given dynamic equation of the CIPS is linearized to obtain a series of linearized models at seven different pendulum angles. Then, seven model predictive controllers (MPCs) are designed based on the above-linearized models, respectively. Introducing the idea of the gain-schedule, a gain-scheduled MPC (GSMPC) is designed to switch one of these seven MPCs to realize the control objective of the CIPS, according to the actual pendulum angle of the SIP during the control process. Finally, multi-group simulations that consider the friction and disturbances of the CIPS are implemented to demonstrate the effectiveness of the proposed gain-scheduled model predictive control method.

Airborne geophysical surveys serve as an effective tool for litho-structural mapping, providing extensive and high-resolution underground information. They offer vital data support for the interpretation and analysis of lithologies and structures, complementing surface geological mapping. In the study area of the Nanpanjiang-Youjiang metallogenic belt in southeast China, we obtained high-resolution aeromagnetic and gamma-ray spectrometry data covering an area of approximately 27,000 km2, which facilitated the conduct of litho-structural mapping. The total magnetic intensity, reduction to the pole, and directional derivative maps generated from the aeromagnetic data, efficiently identified concealed rocks and faults. Additionally, the total count, potassium, thorium to potassium ratio, and ternary maps generated from the airborne gamma-ray spectrometry data demonstrated advantages in distinguishing carbonate rocks from clastic rocks. They also provided more comprehensive geological information, refining the structural strike and location interpreted by the aeromagnetic data. The litho-structural map produced in this study significantly contributes to our understanding of the structures in the Nanpanjiang area and offers valuable guidance for successful mineral exploration endeavors.

In the present work, microalloying is put forward to improve the microstructure and tensile properties of A356.2-0.15Fe (wt.%) alloy by the co-addition of trace Mn and Mg. A suitable Mn/Fe mass ratio of 0.5 is obtained for alloys with 0.15Fe. The yield strength, ultimate tensile strength, and elongation of the A356.2-0.15Fe alloy with an Mn/Fe ratio of 0.5 and containing 0.42 wt.% Mg is 179 MPa, 286 MPa, and 9.1%, respectively, which is acceptable for automotive wheel hub applications. Optical microscopy (OM), scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and electron-probe microanalyzer (EPMA) methods are used to characterize the microstructure of the alloys. The results indicate that Mn addition promotes the transformation of the acicular β-Al5FeSi phase to the block-shaped α-Al(Fe, Mn)Si phase. The ratio of length/width of the Fe-rich phase in the alloy is reduced by 78.8% with an Mn/Fe ratio of 0.5 and containing 0.35 wt.% Mg, compared with that of the alloy without Mn addition and containing 0.35 wt.% Mg. The addition of Mg reduces the secondary dendrite arm spacing (SDAS) from 26.1 μm to 20.9 μm. The volume fraction of the precipitated Mg2Si phase in the alloy containing 0.42 wt.% Mg increases by 60% compared with that in the alloy containing 0.35 wt.% Mg. The morphology transformation of the Fe-rich phase, the reduction of SDAS, and the increase in volume fraction of precipitated Mg2Si phase comprehensively contribute to the improvement of A356.2-0.15Fe alloy. The microstructure evolution mechanism and the effect of microstructure on tensile properties are analyzed and discussed.

In recent years, the development of airborne magnetic survey technology based on unmanned aerial vehicles (UAVs) has been rapidly advancing. The commonly used systems are the fixed-wing UAV-based, multi-rotors UAV-based, and unmanned helicopters-based magnetic survey systems. And, a type of hybrid UAV that uses a vertical takeoff and landing (VTOL) and fixed-wing cruise mode is increasingly being used to carry airborne magnetic survey systems. To meet the requirements of most UAVs for small-sized and lightweight payloads, a miniature magnetic survey system was developed and integrated into a hybrid fixed-wing UAV and formed an aeromagnetic survey system. And, a peripheral mineral exploration test was conducted in a known porphyry copper–gold deposit in southeastern China using the system. By processing the collected magnetic data with 3D inversion of susceptibilities, potential ore-bearing intrusive rocks were quickly identified and delineated, providing clues for peripheral mineral exploration in the mining area.

Determining mineral prospecting targets is crucial for mineral prediction and evaluation. In this study, an evaluation index system for solid mineral exploration and metallogenic target assessment was established using the Analytic Hierarchy Process (AHP) for the Naoniushan area (China). Furthermore, an integrated model combining geology–aerogeophysics–geochemistry was developed for copper, lead, zinc, silver, and other polymetallic deposits. The information content of each index in the model was reasonably assigned, and the mineral prospecting targets in the central and southern parts of the Daxinganling were recommended. By focusing on the copper polymetallic mineral prospecting target in the Naoniushan area, this paper demonstrates that the AHP method can comprehensively consider various influencing factors and their interactions, realize a reasonable division of the optimal mineral prospecting target, and reflect the key factors affecting the mineral prospecting target to a certain extent. Importantly, this approach reduces the influence of human subjective factors, and the optimization results are objective and scientifically grounded.

In the economically developed Beijing–Tianjin–Hebei region, magnetotelluric data are susceptible to contamination from cultural noise, which can be caused, for example, by urban stray currents, high-speed railways, or high-voltage lines. The multiple remote references method is an effective tool that can be used to suppress interference and improve signal-to-noise ratios. Therefore, this paper first introduces the basic principles of multiple remote references and then takes high-speed railway noise as an example. The characteristics of the time domain and frequency domain of the high-speed railway noise signals are analyzed. Then, we use two remote reference stations (with a single remote reference and multiple remote references) to process the data interfering with the high-speed railway and compare the results. Finally, the multiple remote references method is used to process the data for the entire section. Coupled with the known geological and seismic data, the inversion results well-reflect the deep underground geological structure.

Fault diagnosis and health condition monitoring have always been critical issues in the engineering research community [...]

Advanced sensing, fault diagnosis, and structural health management are important parts of the maintenance strategy of modern industries [...]

When considering the transition probability matrix of ordinal patterns, transition permutation entropy (TPE) can effectively extract fault features by quantifying the irregularity and complexity of signals. However, TPE can only characterize the complexity of the vibration signals at a single scale. Therefore, a multiscale transition permutation entropy (MTPE) technique has been proposed. However, the original multiscale method still has some inherent defects in the coarse-grained process, such as considerably shortening the length of time series at large scale, which leads to a low entropy evaluation accuracy. In order to solve these problems, a composite multiscale transition permutation entropy (CMTPE) method was proposed in order to improve the incomplete coarse-grained analysis of MTPE by avoiding the loss of some key information in the original fault signals, and to improve the performance of feature extraction, robustness to noise, and accuracy of entropy estimation. A fault diagnosis strategy based on CMTPE and an extreme learning machine (ELM) was proposed. Both simulation and experimental signals verified the advantages of the proposed CMTPE method. The results show that, compared with other comparison strategies, this strategy has better robustness, and can carry out feature recognition and bearing fault diagnosis more accurately and with improved stability.

Increasing and improving the critical transition temperature (TC), current density (JC) and the Meissner effect (HC) of conventional superconductors are the most important problems in superconductivity research, but progress has been slow for many years. In this study, by introducing the p-n junction nanostructured electroluminescent inhomogeneous phase with a red wavelength to realize energy injection, we found the improved property of smart meta-superconductors MgB2, the critical transition temperature TC increases by 0.8 K, the current density JC increases by 37%, and the diamagnetism of the Meissner effect HC also significantly improved, compared with pure MgB2. Compared with the previous yttrium oxide inhomogeneous phase, the p-n junction has a higher luminescence intensity, a longer stable life and simpler external field requirements. The coupling between superconducting electrons and surface plasmon polaritons may be explained by this phenomenon. The realization of smart meta-superconductor by the electroluminescent inhomogeneous phase provides a new way to improve the performance of superconductors.

Most cross-domain intelligent diagnosis approaches presume that the health states in training datasets are consistent with those in testing. However, it is usually difficult and expensive to collect samples under all failure states during the training stage in actual engineering; this causes the training dataset to be incomplete. These existing methods may not be favorably implemented with an incomplete training dataset. To address this problem, a novel deep-learning-based model called partial transfer ensemble learning framework (PT-ELF) is proposed in this paper. The major procedures of this study consist of three steps. First, the missing health states in the training dataset are supplemented by another dataset. Second, since the training dataset is drawn from two different distributions, a partial transfer mechanism is explored to train a weak global classifier and two partial domain adaptation classifiers. Third, a particular ensemble strategy combines these classifiers with different classification ranges and capabilities to obtain the final diagnosis result. Two case studies are used to validate our method. Results indicate that our method can provide robust diagnosis results based on an incomplete source domain under variable working conditions.

The smart meta-superconductor MgB2 and Bi(Pb)SrCaCuO increase the superconducting transition temperature (TC), but the changes in the transport critical current density (JC) and Meissner effect are still unknown. Here, we investigated the JC and Meissner effect of smart meta-superconductor MgB2 and Bi(Pb)SrCaCuO. The use of the standard four-probe method shows that Y2O3:Eu3+ and Y2O3:Eu3++Ag inhomogeneous phase significantly increase the JC, and JC decreases to a minimum value at a higher temperature. The Meissner effect was measured by direct current magnetization. The doping of Y2O3:Eu3+ and Y2O3:Eu3++Ag luminescent inhomogeneous phase causes a Meissner effect of MgB2 and Bi(Pb)SrCaCuO at a higher temperature, while the non-luminescent dopant reduces the temperature at which samples have Meissner effect. The introduction of luminescent inhomogeneous phase in conventional MgB2 and copper oxide high-temperature Bi(Pb)SrCaCuO superconductor increases the TC and JC, and Meissner effect is exerted at higher temperature. Therefore, smart meta-superconductivity is suitable for conventional and copper oxide high-temperature superconductors.

Rotating machinery is part and parcel of modern industrial applications [...]

The vibration signal of gearboxes contains abundant fault information, which can be used for condition monitoring. However, vibration signal is ineffective for some non-structural failures. In order to resolve this dilemma, infrared thermal images are introduced to combine with vibration signals via fusion domain-adaptation convolutional neural network (FDACNN), which can diagnose both structural and non-structural failures under various working conditions. First, the measured raw signals are converted into frequency and squared envelope spectrum to characterize the health states of the gearbox. Second, the sequences of the frequency and squared envelope spectrum are arranged into two-dimensional format, which are combined with infrared thermal images to form fusion data. Finally, the adversarial network is introduced to realize the state recognition of structural and non-structural faults in the unlabeled target domain. An experiment of gearbox test rigs was used for effectiveness validation by measuring both vibration and infrared thermal images. The results suggest that the proposed FDACNN method performs best in cross-domain fault diagnosis of gearboxes via multi-source heterogeneous data compared with the other four methods.

Incorporating with inhomogeneous phases with high electroluminescence (EL) intensity to prepare smart meta-superconductors (SMSCs) is an effective method for increasing the superconducting transition temperature (Tc) and has been confirmed in both MgB2 and Bi(Pb)SrCaCuO systems. However, the increase of ΔTc (ΔTc = Tc ‒ Tcpure) has been quite small because of the low optimal concentrations of inhomogeneous phases. In this work, three kinds of MgB2 raw materials, namely, aMgB2, bMgB2, and cMgB2, were prepared with particle sizes decreasing in order. Inhomogeneous phases, Y2O3:Eu3+ and Y2O3:Eu3+/Ag, were also prepared and doped into MgB2 to study the influence of doping concentration on the ΔTc of MgB2 with different particle sizes. Results show that reducing the MgB2 particle size increases the optimal doping concentration of inhomogeneous phases, thereby increasing ΔTc. The optimal doping concentrations for aMgB2, bMgB2, and cMgB2 are 0.5%, 0.8%, and 1.2%, respectively. The corresponding ΔTc values are 0.4, 0.9, and 1.2 K, respectively. This work open a new approach to reinforcing increase of ΔTc in MgB2 SMSCs.

A smart meta-superconductor Bi(Pb)SrCaCuO (B(P)SCCO) may increase the critical transition temperature (TC) of B(P)SCCO by electroluminescence (EL) energy injection of inhomogeneous phases. However, the increase amplitude ΔTC (ΔTC=TC−TC,pure) of TC is relatively small. In this study, a smart meta-superconductor B(P)SCCO with different matrix sizes was designed. Three kinds of raw materials with different particle sizes were used, and different series of Y2O3:Sm3+, Y2O3, Y2O3:Eu3+, and Y2O3:Eu3++Ag-doped samples and pure B(P)SCCO were prepared. Results indicated that the TC of the Y2O3 or Y2O3:Sm3+ non-luminescent dopant doping sample is lower than that of pure B(P)SCCO. However, the TC of the Y2O3:Eu3++Ag or Y2O3:Eu3+ luminescent inhomogeneous phase doping sample is higher than that of pure B(P)SCCO. With the decrease of the raw material particle size from 30 to 5 μm, the particle size of the B(P)SCCO superconducting matrix in the prepared samples decreases, and the doping content of the Y2O3:Eu3++Ag or Y2O3:Eu3+ increases from 0.2% to 0.4%. Meanwhile, the increase of the inhomogeneous phase content enhances the ΔTC. When the particle size of raw material is 5 μm, the doping concentration of the luminescent inhomogeneous phase can be increased to 0.4%. At this time, the zero-resistance temperature and onset transition temperature of the Y2O3:Eu3++Ag doped sample are 4 and 6.3 K higher than those of pure B(P)SCCO, respectively.

Three-dimensional hand detection from a single RGB-D image is an important technology which supports many useful applications. Practically, it is challenging to robustly detect human hands in unconstrained environments because the RGB-D channels can be affected by many uncontrollable factors, such as light changes. To tackle this problem, we propose a 3D hand detection approach which improves the robustness and accuracy by adaptively fusing the complementary features extracted from the RGB-D channels. Using the fused RGB-D feature, the 2D bounding boxes of hands are detected first, and then the 3D locations along the z-axis are estimated through a cascaded network. Furthermore, we represent a challenging RGB-D hand detection dataset collected in unconstrained environments. Different from previous works which primarily rely on either the RGB or D channel, we adaptively fuse the RGB-D channels for hand detection. Specifically, evaluation results show that the D-channel is crucial for hand detection in unconstrained environments. Our RGB-D fusion-based approach significantly improves the hand detection accuracy from 69.1 to 74.1 comparing to one of the most state-of-the-art RGB-based hand detectors. The existing RGB- or D-based methods are unstable in unseen lighting conditions: in dark conditions, the accuracy of the RGB-based method significantly drops to 48.9, and in back-light conditions, the accuracy of the D-based method dramatically drops to 28.3. Compared with these methods, our RGB-D fusion based approach is much more robust without accuracy degrading, and our detection results are 62.5 and 65.9, respectively, in these two extreme lighting conditions for accuracy.

The health condition monitoring of rotating machinery can avoid the disastrous failure and guarantee the safe operation. The vibration-based fault diagnosis shows the most attractive character for fault diagnosis of rotating machinery (FDRM). Recently, Lempel-Ziv complexity (LZC) has been investigated as an effective tool for FDRM. However, the LZC only performs single-scale analysis, which is not suitable to extract the fault features embedded in vibrational signal over multiple scales. In this paper, a novel complexity analysis algorithm, called hierarchical Lempel-Ziv complexity (HLZC), was developed to extract the fault characteristics of rotating machinery. The proposed HLZC method considers the fault information hidden in both low-frequency and high-frequency components, resulting in a more accurate fault feature extraction. The superiority of the proposed HLZC method in detecting the periodical impulses was validated by using simulated signals. Meanwhile, two experimental signals were utilized to prove the effectiveness of the proposed HLZC method in extracting fault information. Results show that the proposed HLZC method had the best diagnosing performance compared with LZC and multi-scale Lempel-Ziv complexity methods.

Hand detection is a crucial pre-processing procedure for many human hand related computer vision tasks, such as hand pose estimation, hand gesture recognition, human activity analysis, and so on. However, reliably detecting multiple hands from cluttering scenes remains to be a challenging task because of complex appearance diversities of dexterous human hands (e.g., different hand shapes, skin colors, illuminations, orientations, and scales, etc.) in color images. To tackle this problem, an accurate hand detection method is proposed to reliably detect multiple hands from a single color image using a hybrid detection/reconstruction convolutional neural networks (CNN) framework, in which regions of hands are detected and appearances of hands are reconstructed in parallel by sharing features extracted from a region proposal layer, and the proposed model is trained in an end-to-end manner. Furthermore, it is observed that the generative adversarial network (GAN) could further boost the detection performance by generating more realistic hand appearances. The experimental results show that the proposed approach outperforms the state-of-the-art on public challenging hand detection benchmarks.

Hand pose estimation is a critical technology of computer vision and human-computer interaction. Deep-learning methods require a considerable amount of tagged data. Accordingly, numerous labeled training data are required. This paper aims to generate depth hand images. Given a ground-truth 3D hand pose, the developed method can generate depth hand images. To be specific, a ground truth can be 3D hand poses with the hand structure contained, while the synthesized image has an identical size to that of the training image and a similar visual appearance to the training set. The developed method, inspired by the progress in the generative adversarial network (GAN) and image-style transfer, helps model the latent statistical relationship between the ground-truth hand pose and the corresponding depth hand image. The images synthesized using the developed method are demonstrated to be feasible for enhancing performance. On public hand pose datasets (NYU, MSRA, ICVL), comprehensive experiments prove that the developed method outperforms the existing works.

As an important machine component, the gearbox is widely used in industry for power transmission. Condition monitoring (CM) of a gearbox is critical to provide timely information for undertaking necessary maintenance actions. Massive research efforts have been made in the last two decades to develop vibration-based techniques. However, vibration-based methods usually include several inherent shortages including contact measurement, localized information, noise contamination, and high computation costs, making it difficult to be a cost-effective CM technique. In this paper, infrared thermal (IRT) images, which can contain information covering a large area and acquired remotely, are based on developing a cost-effective CM method. Moreover, a convolutional neural network (CNN) is employed to automatically process the raw IRT images for attaining more comprehensive feature parameters, which avoids the deficiency of incomplete information caused by various feature-extraction methods in vibration analysis. Thus, an IRT–CNN method is developed to achieve online remote monitoring of a gearbox. The performance evaluation based on a bevel gearbox shows that the proposed method can achieve nearly 100% correctness in identifying several common gear faults such as tooth pitting, cracks, and breakages and their compounds. It is also especially robust to ambient temperature changes. In addition, IRT also significantly outperforms its vibration-based counterparts.

Multi-scale permutation entropy (MPE) is a statistic indicator to detect nonlinear dynamic changes in time series, which has merits of high calculation efficiency, good robust ability, and independence from prior knowledge, etc. However, the performance of MPE is dependent on the parameter selection of embedding dimension and time delay. To complete the automatic parameter selection of MPE, a novel parameter optimization strategy of MPE is proposed, namely optimized multi-scale permutation entropy (OMPE). In the OMPE method, an improved Cao method is proposed to adaptively select the embedding dimension. Meanwhile, the time delay is determined based on mutual information. To verify the effectiveness of OMPE method, a simulated signal and two experimental signals are used for validation. Results demonstrate that the proposed OMPE method has a better feature extraction ability comparing with existing MPE methods.