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Recently, remote sensing (RS) technology are becoming an increasingly important technology in estimating forest growing stem volume (GSV), and the saturation issue of spectral variables from various optical sensors severely hinders the improvement of mapping forest GSV, especially in the planted forest with high GSV. Forest canopy height is widely considered as one of the major factors to increase the saturation levels in mapping GSV. However, it is rather difficult to invert the forest canopy height without precisely external DEM for large regions. In this study, the canopy height model (CHM) was derived from ZY-3 stereo images with subtracting open-sourced external DEM and the response of saturation levels was analyzed by adding forest height in the planted coniferous forest (Larch and Chinese pine). To further describe the relationships between the forest height and saturation levels, five datasets with five estimation models (Linear, MLR, SVR, KNN, and RF) and three methods of variable selection (Stepwise, LASSO, and Pearson) were applied to estimate the forest GSV using corrected CHM and 49 alternative variables extracted from ZY-3 multispectral images. Meanwhile, a spherical model was employed to quantitatively describe the saturation levels of combined variables. The results showed that values of relative root means square error were decreased from 29.3% to 25% for Larch and from 26.5% to 22.2% for Chinese pine after adding the corrected CHM, respectively. Meanwhile, the saturation level of each combined variable set was successfully determined by the spherical model. The results illustrated that the saturation levels of GSV were significantly increased by adding corrected CHM from open-sourced external DEM. Specially, the averaged saturation levels were increased from 220 m3/ha to nearly 300 m3/ha for Chinese pine and from 150 m3/ha to 220 m3/ha for Larch, respectively. It is proved that ZY-3 stereo and multispectral images have great potential for accurate estimation of forest GSV by delaying the saturation levels using extracted CHM with open-sourced external DEM.

A recently modern stochastic optimization algorithm has been developed by observing the life of slime mold physarum polycephalum in nature. The algorithm is called the slime mold algorithm (SMA) with an excellent exploratory capacity and exploitation inclination. Still, slipping into optimal local is easy to happen and slowly converges speed while dealing with complicated problems. This article proposes a new process of improving SMA (namely ISMA) by adapting the weight coefficient and cooperating the reverse learning strategy in the expression of agents updating locations to enhance the algorithm’s optimization performance. Many selected benchmark functions and the optimal operation of cascade reservoirs are applied to evaluate the performance of the proposed algorithm. Comparisons of the proposed approach’s results with the various algorithms under the case situations show that the recommended solution produces better performance than the different competing algorithms.

Lithium-ion (Li-ion) batteries play a substantial role in energy storage solutions for modern-day technologies such as hand-held consumer electronics, aerospace, electric vehicles, and renewable energy systems. For Li-ion batteries, designing a high-quality battery charging algorithm is essential since it has significant influences on the performance and lifetime of Li-ion batteries. The objectives of a high-performance charger include high charging efficiency, short charging time, and long cycle life. In this paper, a model predictive control based charging algorithm is proposed, the presented technique aims to simultaneously reduce the charging time, and the temperature rise during charging. In this study, the coulomb counting method is utilized to calculate the future state-of-charge and an artificial neural network trained by experimental data is also applied to predict the future temperature rise. Comparing with the widely employed constant current-constant voltage charging method, the proposed charging technique can improve the charging time and the average temperature rise by 1.2 % and 4.13 %, respectively.

Neurodegenerative diseases are common progressive nervous system disorders that show intricate clinical patterns. The gait fluctuations reflect the physiology and pathologic alterations in the locomotor control system. Using gait fluctuations for disease state evaluation is an essential way for clinical trials and healthcare monitoring. The classification of gait fluctuations helps improve the life quality and enhance clinical diagnosis ability in neuro-degenerative patients. In this work, we firstly embed the time series of multiple gait fluctuations into the phase space. Then we use persistent homology to extract the topological signatures of barcodes. Together with a random forest classifier, we proposed a topological motion analysis (TMA) framework to analyze the gait fluctuations. Further, we proposed a comprehensive comparison study using the TMA framework in the neuro-degenerative classification tasks for stance-, stride-, and swing-based gait fluctuations. In the tasks of comparing amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), and Parkinson's disease (PD) to the healthy control (HC) group, the best-achieved AUC scores were 0.9135, 0.9906 and 0.9667 respectively, which show the effectiveness of TMA framework. In summary, our study proposed a TMA framework towards gait fluctuations classification in the neuro-degenerative analysis tasks. The proposed method shows promising clinical application value in earlier interventions and state monitoring for neurodegenerative patients.

This study reviews the superiority and limitations of the Singular Value Decomposition (SVD)-based video watermarking scheme. First applies key frame selection to the video sequence to obtain an appropriate video frame for the purpose of watermark embedding in this paper. The Fibonacci-Lucas image transformation scrambles the grayscale watermark image before the embedding process. The former scheme satisfies the imperceptibility and robustness aspects. However, it can suffer from the False-Positive-Problem (FPP) in which a counterfeit watermark image can be easily reconstructed by a malicious attacker. This study presents a new technique for overcoming the FPP of the aforementioned scheme. The proposed method embeds the principal components of the watermark image rather than the Singular Value Matrix (SVM) into the host video. As results, the proposed methods yield promising results in terms of imperceptibility as well as robustness. At the same time, it solves the FPP. A theoretical analysis of embedded watermark information is also provided in this work. The theoretical analysis meets the experimental-based results in the SVD-based video watermarking scheme.

Consisting of the traditional distribution transformer (TDT) and two voltage source inverters, the hybrid distribution transformer (HDT) can realize the voltage level conversion while solving various power quality problems in the power distribution system. Because the reliability and service life of power electronic devices are much lower than the low-frequency transformer, the fault conditions of HDT are more complex and frequent. This paper proposes a DC-link voltage fault-tolerant control method to ensure the DC-link voltage can maintain the reference value even when a voltage source inverter fails. Multiple protection strategies for HDT are also proposed to remove the faulty part and ensure the normal operation of other functions in different fault situations. Simulation results are provided to verify the effectiveness of the proposed multiple protection strategies of HDT.

We have demonstrated a 1.3 μm DFB laser with a threshold current of 20 mA and a slope efficiency of over 0.25 W/A for high-power and free-space communication. Even under high-temperature conditions, it can still maintain outstanding single-mode characteristics. We also systematically compare the degree of the wavelength shift at different temperatures and injected currents. At the same time, we observed that the microwave responses are different with the lasing condition is switching between the DFB and Fabry-Perot (FP) mode.

Output buffer circuits are often adopted for driving next stage Integrated Circuit (IC). Most Input/ Output (I/O) circuit designers use the output buffer circuits, but cannot majorly modify the circuits. In this study, output buffer circuits are derived from Karnaugh maps so I/O designers can use only one pen and one paper to draw output buffer circuits. They don’t need to look at any existed circuits, but just realize output design principals. Then, they can easily create output buffer circuits.

The current-illuminance characteristic curve of most LEDs is nonlinear. Consequently, under the same lighting power, the PWM dimming cannot obtain the best luminous conversion efficiency that is specified. This paper focuses on the further improvement of LED luminous efficiency and studies current dimming techniques with varied current sensing resistance. A prototype constructed for a 115W LED driver is developed with the devised dimming method realized by a digital signal controller (DSC). Experimental results verify the feasibility and performance. Comparing with conventional PWM dimming, under the same illuminance, the driver average output power is reduced by 17.08 %. The improvement in average illuminance under the same output power is 13.66 %.

During the expansion process, the structure and sealed integrity of a new type expandable casing joint with metal sealing were explored by the finite element explicit dynamic analysis method, the nonlinear analysis is used in the expansion process of metal sealing joint of expansion casing under different friction coefficients between expansion cone and inner wall of expansion casing, while the key technical parameters such as effective stress, residual stress and contact force on the sealing surface of the expandable casing joint were obtained. When the expansion process is over, the V on Mises peak stress and residual stress increase with the rise of the friction coefficient. In order to maintain the structural integrity of the joint, the friction coefficient should be controlled below 0.12. Stress is focused on the thread teeth in the expansion process, and the residual peak stress is concentrated at the root of internal thread after expansion. Increasing the friction coefficient moderately is beneficial to increase the contact force between sealing surfaces after expansion, and thus to improve the sealing performance of the joint. Considering the structure and sealed integrity of the joint, the friction coefficient should be controlled at about 0.1. The results of the paper research had reference value for the design of expandable casing joint with metal sealing structure.

In this paper, a fault feature extraction algorithm model of distribution network based on wavelet entropy is designed, and combined with a large number of simulation data and field data for test and analysis, to improve the accuracy of line fault diagnosis and troubleshooting. The case verification results show that the feature extraction algorithm and feature gene library based on wavelet entropy can effectively identify the normal state, three-phase short circuit fault, two-phase short-circuit fault, two-phase grounding fault and single-phase grounding fault of distribution network transient signals, which meets the requirements of sustainable development of distribution network, and has good economic and social benefits.

Virtual simulation technology can reproduce the actual conditions and make the experimental project more engineering. The technology can make students free from the limitation of time and space and be able to practice anytime and anywhere. It not only greatly improves students' learning efficiency, but also promotes the connection between curriculum and practice teaching and strengthens the integration of theory teaching and practice teaching. At the same time, the automatic scoring function of virtual simulation system can help students get timely feedback, so as to find and solve problems in time, which plays a very important role in promoting the timely adjustment of curriculum arrangement and deepening of teaching reform. Based on idea of "student-centered", the course of electronic control of machine tool and PLC introduces the virtual simulation technology and carries out the course reform. This paper introduces the process of curriculum reform from the aspects of course reform ideas, concrete measures and evaluation reform. From the perspective of student feedback, course reform not only improves the subjective initiative of students, but also improves the teaching effect significantly.

Parameter recognition way of well test parameters and fault diagnosis based on BP neural network are very effective. Firstly, ten models are established, they are all researched by team members. According to the characteristics and parameter algorithm of BP neural network, the test well model is optimized, the neural network is constructed, in order to make parameter recognition. And fault diagnosis for those ten well test models. Finally, some examples prove that this method is effective.

In order to make the thermo-hygrometer calibrator more intellectualized, an automatic indication recognition algorithm of dual-pointer meter is proposed. Firstly, Hough circle transform algorithm is used to segment the image and intercept the dial; secondly, the color feature of the image is used to detect the datum point and correct the image; then, the image of pointer is extracted by the improved Zhang thinning algorithm and progressive probabilistic Hough transform algorithm; finally, the algorithm is evaluated by the images of meter captured in an experimental platform of the thermo-hygrometer calibrator. The results show that the algorithm proposed in this paper can recognize the indication of the meter with high accuracy and robustness.

To improve the accuracy of partial demagnetization fault diagnosis for permanent magnet synchronous motor (PMSM), an improved fuzzy extreme learning machine (F-ELM) algorithm is constructed by integrating fuzzy theory into the extreme learning machine (ELM) in this paper. Firstly, a PMSM field-circuit coupling simulation system under vector control is established by employing finite element analysis method. Secondly, the feature samples affecting classification accuracy can be obtained using wavelet packet decomposition (WPD). Thirdly, by taking the imbalance of demagnetization feature into consideration, a new type of improved ELM, i.e., F-ELM, is proposed by associating input layer with a fuzzy membership. Finally, a comparison with the accuracy of back-propagation neural network (BPNN), support vector machine (SVM), and ELM is performed. The experimental results show that the proposed method outperforms the existing machine learning methods, and can effectively diagnose the partial demagnetization fault of PMSM.

Electricity price is crucial to the power system's reliability and economy, while long-term electricity prices can not avoid the deviation between the contracted electricity and the physical execution of electricity. Many electricity markets introduced on-demand spot prices to narrow the gap and balance power supply and demand. That will cause transmit electricity congestion due to users' unwillingness to accept higher prices. Previous works modeled the spot pricing auction process as a discrete multi-objective optimization problem according to auction rules with various costs and constraints. Most approaches ignored the users' sensitivity to reliability and price. Furtherly, the state space increases exponentially with the increases of users, which costs a long time to obtain stable convergence price and results in a negative price. We propose a deep reinforcement learning-based spot reliability pricing model that defines the balance as a state denotes price selection as action, and adopts the balance of supply and demand as a reward function. Besides that, we introduce users' sensitivity to reliability and price as penalty factors of the function. After comparing several reinforcement learning models' performance, we choose Policy-based Deep Q-Learning Networks (PDQN) to determine our reliability pricing strategy. Experiments show that the model ensures that spot prices converge to balance in 5 iterations and reduce the congestion rate by half compared to other methods.

Graph Convolutional Networks (GCNs) have achieved state-of-the-art performance on node classification. However, recent works have shown that GCNs are vulnerable to adversarial attacks, such as additions or deletions of adversarially-chosen edges in the graph, in order to mislead the node classification algorithms. How can we design robust GCNs that are resistant to such adversarial attacks? More challengingly, how can we do this in a way that is provably robust? We propose a robust node classification approach based on a low-pass ‘message passing’ mechanism, that (a) reduces the effectiveness of adversarial attacks in experiments, and (b) provides theoretical guarantees against adversarial attacks. Our approach can be embedded into the existing GCN architectures to enhance their robustness. Empirical results show that our loss-pass method effectively improves the performance of multiple GCNs under miscellaneous perturbations and helps them to achieve superior performance on various graphs.

Faced with today's global environmental sustainability issues, air pollution has always been one of the problems people want to solve. In Taiwan, the government is also actively promoting electric scooters (E-Scooters) to replace traditional bikes to reduce the accompanying air pollution. Although some major manufacturers have launched products of E-Scooters, related services such as the parking priority service, are still underdeveloped. This study will use the service design method to establish innovative parking services for E-Scooters users. First, we use focus group interviews to understand the pain points faced by today's users and use a persona to present the situation of users. Then we analyze the user's scenario and needs through the user journey map. The third step is to analyze the user's needs and technology through quality function deployment (QFD) to obtain the corresponding priority level. After completing the above steps, we will construct related design prototypes and service blueprints. Finally, we made a preliminary evaluation through the Kano model. The results of this research show that users are looking forward to enjoying the proposed innovative services, such as the two functions of charging/battery exchange services and parking records. With this study, the government or enterprises in the future can have a reference when constructing E-Scooter services, and have a elementary understanding of the priority of technology development.