A low-power NPN-based bandgap voltage reference (BGR) over an ultra-wide temperature range is presented. The conventional NPN-based BGRs cannot maintain a low-temperature coefficient (TC) over an ultra-wide temperature range due to the inherent substrate leakage current of the NPN bipolar junction transistors (BJT) in the high-temperature range. This work introduces a new NPN-based BGR unaffected by substrate leakage current and receives low TC over the range of -40℃ to 150℃. The proposed circuit was fabricated in a 180 nm CMOS process. It consumes 2uA from a 4V power supply, and its average TC is 14.89ppm/℃. Also, the average line sensitivity is 0.039%/V.

Endoscopic renal surgeries have high re-operation rates, particularly for lower volume surgeons. Due to the limited field and depth of view of current endoscopes, mentally mapping preoperative computed tomography (CT) images of patient anatomy to the surgical field is challenging. The inability to completely navigate the intrarenal collecting system leads to missed kidney stones and tumors, subsequently raising recurrence rates. We propose a guidance system to estimate the endoscope positions within the CT to reduce re-operation rates. We use a Structure from Motion algorithm to reconstruct the kidney collecting system from the endoscope videos. In addition, we segment the kidney collecting system from CT scans using 3D U-Net to create a 3D model. We can then register the two collecting system representations to provide information on the relative endoscope position. We demonstrate correct reconstruction and localization of intrarenal anatomy and endoscope position. Furthermore, we create a 3D map supported by the RGB endoscope images to reduce the burden of mental mapping during surgery. The proposed reconstruction pipeline has been validated for guidance. It can reduce the mental burden for surgeons and is a step towards our long-term goal of reducing re-operation rates in kidney stone surgery.

The effect of niobium ion with high valence doping on high voltage LiNi0.5Mn1.5O4 materials was investigated. LiNi0.5Mn1.5-xNbxO4 was prepared by doping high-valent niobium ion into LiNi0.5Mn1.5O4 material using the organic assisted combustion method. The experimental samples were characterized by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy and electrochemical impedance analysis. The experimental results show that the doping with high valence niobium ion change the orientation of the crystal plane growth of spinel particles, and the morphology of these particles change from the octahedral shape before doping to the spherical shape after doping. With the increase of doping amount, the crystal structure changes gradually, resulting in the Li0.96Nb1.01O3 impurity phase. The doping of high valence niobium ion increases the content of Mn3+ in the material, resulting in the appearance of a 4 V discharge platform, and the formation of a 4.7 V and 4 V discharge platform. The doping of Nb can improve the cycling stability of LiNi0.5Mn1.5O4 material, but the specific capacity of the material is reduced.

Operating experience has shown that vibration can cause pressure fluctuations and initiate bubbles at the oil gap, which seriously threatens the insulation properties of oil-immersed power equipment. The main aim of this study was to determine the characteristics and mechanism of bubble initiation at the transformer oil gap under vibration in this paper. The results show the existence of the vibration-dependent bubbling phenomenon at the oil gap. Interestingly, bubbles initiate during the process of gap expansion, and the evolution of bubble morphology was further analyzed. The threshold characteristic of the bubble initiation process was found, and the relevant influencing factors were experimentally obtained. In the range considered in this study, higher gas content, higher temperature, and lower moisture content are conducive to bubble initiation at the oil gap under vibration, while vibrational frequency does not affect the threshold characteristic of bubble initiation process. The vibration-dependent bubbling phenomenon can be attributed to the vibration-induced cavitation process caused by the decrease in pressure at the oil gap. The influence mechanism of the above factors on the threshold characteristics of bubble initiation was further discussed. This study contributes to providing a reference for risk assessment of abnormal vibration of oil-immersed power equipment.

Automatic dependent surveillance-broadcast (ADS-B) has been widely used due to its low cost and high precision. The deep learning methods for ADS-B signal classification have achieved a high performance. However, recent studies have shown that deep learning networks are very sensitive and vulnerable to small noise. We propose an ADS-B signal poisoning method based on U-Net. This method can generate poisoned signals. We assign one of ADS-B signal classification networks as the attacked network and another one as the protected network. When poisoned signals are fed into these two well-performed classification networks, the poisoned signal will recognized incorrectly by the attacked network while classified correctly by the protected network. We further propose an Attack-Protect-Similar loss to achieve “triple-win” in leading attacked network poor performance, protected network well performance and the poisoned signals similar to unpoisoned signals. Experimental results show attacked network classifies poisoned signals with a 1.55% classification accuracy, while the protected network classifies rate is still maintained at 99.38%.

Radio frequency (RF) fingerprinting is a challenging and important technique in individual identification of wireless devices. Recent work has used deep learning-based classifiers on ADS-B signal without missing aircraft ID information. However, traditional methods are difficult to obtain well performance accuracy for classical deep learning methods to recognize RF signals. This letter proposes a Gaussian Low-pass Channel Attention Convolution Network (GLCA-Net), where a Gaussian Low-pass Channel Attention module (GLCAM) is designed to extract fingerprint features with low frequency. Particularly, in GLCAM, we design a Frequency-Convolutional Global Average Pooling (F-ConvGAP) module to help channel attention mechanism learn channel weights in frequency domain. Experimental results on the datasets of large-scale real-world ADS-B signals show that our method can achieve an accuracy of 92.08%, which is 6.21% higher than Convolutional Neural Networks.

The multi-scale flexibility coordination of multiple storages is a key technology to enhance the diversified regulation ability of the power system.This paper first considered the interaction mechanism of multi-type storage peak regulation time sequences based on the Euclidian distance, Dynamic time warping distance, and storage correlation distance. A matching index was proposed to consider the temporal correlation, overall distribution characteristics, and dynamic characteristics of the net load and energy storage. The multitype storage coordination mode, including battery storage, pumped storage, and electric vehicles, was formulated, and a collaborative optimal scheduling system architecture of source-grid-load-storage (SGLS) was constructed. To attain a low-carbon economy, a collaborative optimal scheduling model of SGLS considering the dynamic time-series complementarity of multiple energy storage systems was constructed. The Nash equilibrium theory was used to achieve friendly interaction among the source, grid, load, and storage. Then, an improved transfer reinforcement learning algorithm for SGLS was proposed, which used reinforcement learning and transfer learning algorithms combined with K-means clustering and dual-structure experience pool technology. The test results of actual regional power grid data indicated that the proposed strategy can effectively reduce the economic and carbon treatment costs of the system and improve the absorption capacity of renewable energy.

The transparency of machine learning models is central to good practice when they are applied in high stakes applications. Recent developments make this feasible for tabular data, which is prevalent in risk modelling and computer-based decision support across multiple domains including healthcare. Important motivating factors for interpretability are outlined and practical approaches are summarised, signposting the main methods available, with pointers to the supporting literature. A key finding is that any black box classifier making probabilistic predictions of class membership from data in tabular form can be represented with a globally interpretable model without loss of performance.

ECG is a non-invasive way of determining cardiac health by measuring the electrical activity of the heart. We investigate a novel detection technique for feature points P, QRS and T to diagnose various atrial and ventricular cardiovascular anomalies with ECG signals for ambulatory monitoring. Before the system is worthy of field trials, we validated it with several databases and recorded their response. The QRS complex detection is based on the Pan Tompkins Algorithm and difference operation method that provides positive predictivity, sensitivity and false detection rate of 99.29\%, 99.49\% and 1.29 \% respectively. Proposed novel T wave detection provides sensitivity of 97.78\%. Also, proposed P wave detection provides positive predictivity, sensitivity and false detection rate of 99.43\%, 99.4\% and 1.15\% for the control study (normal subjects) and 82.68\%, 94.3\% and 25.4\% for the case (patients with cardiac anomalies) study respectively. Disease detection such as, arrhythmia is based on standard R-R intervals while myocardial infarction is based on the ST-T deviations where the positive predictivity, sensitivity and accuracy are observed to be 94.6\%, 84.2\% and 85\%, respectively. It should be noted that, since the frontal leads are only used, the anterior myocardial infarction cases are detected with the injury pattern in lead \textit{avl} and ST depression in reciprocal leads. Detection of atrial fibrillation is done for both short and long duration signals using statistical methods using interquartile range and standard deviations, giving very high accuracy, 100\% in most cases. The system hardware for obtaining the 2 lead ECG signal is designed using commercially available off the shelf components. Small field validation of the designed system is performed at a Public Health Centre in Gujarat, India with 42 patients (both cases and controls). We achieved 78.5\% accuracy during the field validation.

A novel parameter estimation method is proposed for the permanent magnet synchronous generator (PMSG), which is implemented by an enhanced self-learning particle swarm optimization algorithm with Levy flight (SLPSO), and the problem of lower parameter estimation precision of standard PSO is obviated. This method injects currents of different intensities into the d-axis in a time-sharing manner to solve the problem of equation under-ranking, and the mathematical model for full-rank parameter estimation is developed. The speed term of PSO is simplified to expedite the convergence of PSO, and a strategy with Chaotic decline for the inertia weight of PSO is adopted to strengthen its ability to jump out of the local optimum. Moreover, the self-learning dense fleeing strategy (SLDF) is proposed where particles perform diffusion learning based on population density information and Levy flight, the evolutionary unitary problem and human intervention in the evolutionary process is averted. Furthermore, the memory tempering annealing algorithm (MTA) and greedy algorithm (GA) is integrated into the algorithm, MTA can facilitate the exploration of potentially better regions, and GA for local optimization enhances the convergence speed and accuracy in late stage of the algorithm. Comparing the proposed method with several existing PSO algorithms through simulation and experiments, the experimental data show that the proposed method can effectively track variable parameters under different working conditions and has better robustness.

One of the major bottlenecks of the traditional Current Transformer Energy Harvester (CTEH) is the instable output induced by the wide-range variations of the current in transmission lines. In this work, a novel CTEH capable of generating a stable output is demonstrated by using a core fabricated with saturable magnetic material. The stable output of the CTEH is enabled by the constant voltage-time product in its saturable characteristic. The proposed CTEH is implemented with a resistive load representing the load of electronic devices. When the current in the primary side of the CTEH’s increases from 1A to 1000A, the maximum power on the load can reach about 0.5W, demonstrating the feasibility of using the CTEH with the saturable magnetic core as a quasi-stable power supply.

Most of the first eight pilot regions in China have entered the long-period continuous operation stage, and the second batch of six power spot pilots have started their simulation trial runs; however, the construction of power markets in the hydropower-rich regions of China (i.e., Sichuan and Yunnan) still faces multiple problems, including poor reliability of medium- and long-term power supplies, inefficient hydropower resource utilization and consumption scales, and unstable market prices. In this study, we establish a multi-scale joint clearing model for different market models (considering both long- and short-term operation strategies) and perform simulations of different market models at different time scales, to compare and analyze their optimization results and electricity price fluctuations. The model can stabilize the market price and reasonably reflect the true value of hydropower; it can also promote the consumption of clean energy and ensure the reliability of power supplies in the medium and long term. Finally, suggestions are made regarding the selection of electricity market models, the security of energy supplies, and the design of sustainable development mechanisms for hydropower in hydropower-rich regions, with a view to providing useful references for the development of China’s electricity market.

Considering light absorbing and scattering problems in connection with wavelength can decrease the visibility, contrast and color distortion of images, we propose a new type of convolutional neural network with two training phases. Firstly, the coordinate attention module is integrated into the residual block of the residual group in the backbone network, which is used to strengthen the feature extraction capability of the network. Secondly, since the unrealistic image colors may degrade the image details, an unsupervised method that combines the physical prior knowledge and the real underwater images is proposed to finetune the backbone network. Furthermore, a model protection mechanism is designed to guarantee the successful execution of the training. The experimental results indicate the proposed model can effectively optimize the contrast, color and image quality of the underwater image. Compared with relevant algorithms, our UCIQE and NIQE are respectively 0.525 and 4.149, which further verifies the superiority of the proposed model.

The existing prefabricated wireline construction technology is difficult to accurately measure the real-time length of the deployed wires, resulting in too large construction errors and cannot be popularized and applied. To this end, a length measuring device that can accurately measure the length of the wire during the construction of the prefabricated wire is developed. In terms of the hardware of the length measuring equipment, based on the construction characteristics of the prefabricated wiring and the characteristics of the wire, the photoelectric encoder and the STC8G series single chip microcomputer are used to complete the collection and processing of the wire length data, and the mature and stable LoRa technology is used to complete the wireless transmission of the data. In terms of software program of length measurement equipment, the measurement of wire length data, transmission and interaction of control command signals are realized by optimizing length measurement scheme, interface development of terminal equipment and information interaction program design. Finally, according to the actual working conditions of the prefabricated wiring construction, the mechanical structure of the length measuring equipment is designed, and the assembly is completed. The developed length measuring equipment has been successfully applied to practical projects. According to the wire length measuring equipment, the deviation between the observation sag and the design sag after the wire length is measured by the wire length measuring equipment is 2.0%, which meets the design requirement of ±2.5%.

Multi-terminal high-voltage direct current technology based on voltage-source converter stations (VSC-MTDC) is expected to be one of the most important contributors to the future of electric power systems. In fact, among other features, it has already been shown how this technology can contribute to improve transient stability in power systems by the use of supplementary controllers. Along this line, this paper will investigate in detail how these supplementary controllers affect electromechanical oscillations, by means of small-signal stability analysis. The paper analyses two control strategies based on the modulation of active-power injections (P-WAF) and reactive-power injections (Q-WAF) in the VSC stations which were presened in previous work. Both control strategies use global signals of the frequencies of the VSC-MTDC system and they presented significant improvements on transient stability. The paper will provide guidelines for the design of these type of controllers to improve both, large- and small-disturbance angle stability. Small-signal stability analysis (in Matlab) has been compared with non-linear time domain simulation (in PSS/E) to confirm the results using CIGRE Nordic32A benchmark test system with a VSC-MTDC system. The paper analyses the impact of the controller gains and communication latency on electromechanical-oscillation damping. The main conclusion of the paper is that transient-stability-tailored supplementary controllers in VSC-MTDC systems can be tuned to damp inter-area oscillations too, maintaining their effectiveness.

The target and sea clutter Doppler domains frequently overlap due to the frequent passage of slow ship targets through the sea clutter zone. In this letter, a novel sea clutter suppression method is suggested as a solution to this issue, whose key is a novel singular value zeroing criterion guided by the search results of two-dimensional spectral peaks. Verified by simulations, the method proposed can improve signal-to-clutter ratio (SCR) from -8 dB to 41 dB in the frequency domain and be more effective than the conventional SVD-FRFT method in[7] and improved SVD-FRFT method in[8] .

This study investigates the development of InAs quantum dot (QD) lasers on a InP(001) substrate, utilizing only III-arsenide layers. This approach avoids the issues associated with the use of phosphorus compounds, which are evident in the crystal growth of conventional C/L-band QD lasers, making the manufacturing process safer, simpler, and more cost-effective. The threshold current density of the fabricated QD laser was 633 A/cm2, which is the lowest value for QD lasers in the 1.6 μm-wavelength region. This result suggests a high cost-effectiveness and paved the way toward a large-scale production technology for high-performing C/L/U-band QD lasers.

The tire lateral force control is crucial to vehicle lateral stability. Vehicle side slip and out of control can be prevented effectively by observing accurately the lateral force. Thus, a novel hyperbolic tangent sliding mode observation algorithm (NTSMO) is proposed. The algorithm adopts the longitudinal tire force error as feedback considering vehicle parameter uncertainties and without a complex tire model. First, the on-line verification of the algorithm was carried out by dSPACE to using the experimental data of the real vehicle linear acceleration and deceleration conditions, and comparison of experimental output with different observation algorithms. Furtherly, the simulation under emergency obstacle avoidance conditions and the double-line shifting conditions were conducted to verify the accuracy of the algorithm respectively. Simulation results show that the percentage errors between the tire lateral forces from the proposed NTSMO and the actual data are less than 5.35%, and the prediction accuracy of the NTSMO by 38.78% is higher than that of the sliding mode observation(SMO), which indicates that the NTSMO is superior to the SMO.