The dynamic on-state resistance instability of a high-current cascode multi-GaN-chip power module under high frequency and voltage switching conditions is demonstrated in this paper. The presented double pulse test (DPT) topology is utilized to evaluate switching dependencies on voltage, current, and frequency, showing its versatility in investigating the switching instability of the device. The extended defects in the buffer layer resulted in a decrease in dynamic on-state resistance (RDS-ON) under hard switching conditions. Despite this, no noticeable RDS-ON degradation occurs under harsh switching conditions due to electron de-trapping. This study comprehensively analyzes the dynamic stability of a multi-GaN-chip cascode module with devices.

Reactive power management (RPM) in electric power systems is usually based on a rule-based control derived from the transmission system operator’s experience. This approach faces challenges as the number of decisions and the complexity of the system operation is increasing. With the increasing generation from renewables and the evolution of electricity markets, the available resources must be optimally utilized. In this paper, a comparison is made between the optimization-based approach (OBA) and the experience-based expert approach (EBA) for RPM. The OBA is based on security-constrained optimization with minimum redispatch cost as the objective function for different contingencies. In contrast, the EBA’s actions are based on the system operator’s experience. Comparison is made in terms of the generator redispatch cost, active and reactive power redispatch volume, nodal voltages, and the number of actions to ensure secure operation. The analysis using a reduced model of the target system shows that OBA is more beneficial than EBA, with up to 22% and 42% reduction in redispatch cost and volume, respectively. Moreover, the control decisions from both approaches are seen to be similar. This study aims to show the usefulness of the OBA and motivate TSOs to move towards optimization-based reactive power management

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.

Facial expression has been widely used in clinical practice to assess pain in newborns. However, the inherent visual attention required to make such vital inference is poorly understood. It is also unknown whether this inference occurs differently when comparing health professionals with other adults. To investigate these issues, we have recorded and monitored the pupil size signal of 102 subjects (44 experts, 29 parents, and 29 non-experts) while visually analyzing 20 frontal face images of 10 distinct newborns after a painful procedure and at painless rest. Our experimental results have showed that neonatal pain assessment is more cognitively demanding when analyzing the presence of pain rather than its absence. Moreover, our results disclose that a 2-second exposure to a facial expression is sufficient to make this assessment, regardless whether done by health professionals or non-health ones, suggesting that this highly specific visual task is not driven by clinical experience.

We demonstrate a high-speed 8.5 μm quantum cascade laser with room temperature continuous wave operation. The maximum output power of 141 mW is obtained at 20 ℃. The parasitic capacitance of the device is decreased from 36.6 pF to 7.1 pF by monolithic integrating a π-shape metal contact electrode. This results in an increase in the -3 dB RF modulation bandwidth from 870 MHz to 4.5 GHz compared with the conventional electrode configuration.

This letter reports a 28-GHz multi-port magneto-electric (ME) dipole array for 5G applications. The proposed antenna array enlarges the system polarization diversity with the capability of being utilized as a balanced antenna, a dual-polarized antenna and a circularly polarized antenna. Co-planar waveguide (CPW) lines are used to connect ME dipole radiators to achieve a high gain with simple feeding structure. The proposed antenna array exhibits a -10dB impedance bandwidth of 12.8% and a maximal peak realized gain of 13.52 dBi as a balanced antenna, and exhibits a -10dB impedance bandwidth of 28.57%, a 3-dB axial ratio bandwidth of 16% and a maximal peak realized gain of 12.15 dBi as a circularly polarized antenna.

This paper proposes a recurrent neural network (RNN) based model to segment and classify multiple combined multiple power quality disturbances (PQDs) from the PQD voltage signal. A modified bi-directional long short-term memory (BI-LSTM) model with two different types of attention mechanism is developed. Firstly, an attention gate is added to the basic LSTM cell to reduce the training time and focus the memory on important PQD signal part. Secondly, attention layer is added to the BI-LSTM to obtain the more important part of the voltage signal by assigning weightage to the output of the BI-LSTM model. Finally a SoftMax classifier is applied to classify the combined PQD signal in 96 different combinations. The performance of proposed BI-LSTM model with attention gate and attention layer mechanism is compared with the performance of baseline models based on recurrent neural network (RNN) and convolution neural network (CNN). With this model, the PQD signal is easily segmented from the voltage signal which makes the process of PQD classification more accurate with less computation complexity and in less time.

Today, the elderly population is increasing and they endure many drawbacks, especially defects in their knee joints which lead to improper gait. To solve this problem, their knee joint can be replaced with knee arthroplasty. In this letter, the level of improvement in the human gait before and after total knee arthroplasty (TKA) surgery is investigated using the Dynamic Time Wrapping (DTW) algorithm. For this purpose, several volunteers who have problems with their knees incorporate in a test before and after TKA surgery, and data of gait analysis is collected. Then, the obtained data is compared with a reference using the DTW algorithm. The outcome results illustrate an improvement of 89\% - 97\% by the proposed algorithm after TKA surgery. Therefore, patients can see improvement with high speed and accuracy that result in more promising for using TKA surgery.

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.

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.

This work present a proof-of-concept of a robotic-driven intra-operative scanner designed for knee cartilage lesion repair, part of a system for direct in vivo bioprinting. The proposed system is based on a photogrammetric pipeline, which reconstructs the cartilage and lesion surfaces from sets of photographs acquired by a robotic-handled endoscope, and produces 3D grafts for further printing path planning. A validation on a synthetic phantom is presented, showing that —despite the cartilage smooth and featureless surface— the current prototype can accurately reconstruct osteochondral lesions and their surroundings with mean error values of 0.199 ± 0.096 mm but with noticeable concentration on areas with poor lighting or low photographic coverage. The system can also accurately generate grafts for bioprinting, although with a slight tendency to underestimate the actual lesion sizes, producing grafts with coverage errors of -12.2 ± 3.7, -7.9 ± 4.9 and -15.2 ± 3.4 % for the medio-lateral, antero-posterior and craneo-caudal directions respectively. Improvements in lighting and acquisition for enhancing reconstruction accuracy are planned as future work, as well as integration into a complete bioprinting pipeline and validation with ex vivo phantoms.

Kidney stones require surgical removal when they grow too large to be broken up externally or to pass on their own. Upper tract urothelial carcinoma are also sometimes treated endoscopically in a similar procedure. These surgeries are difficult, particularly for trainees who often miss tumors, stones or stone fragments, requiring re-operation. One cause of difficulty is the high cognitive strain surgeons experience in creating accurate mental models during the endoscopic operation. Furthermore, there are no patient-specific simulators to facilitate training or standardized visualization tools for ureteroscopy despite its high prevalence. We propose ASSIST-U, a system to automatically create realistic ureteroscopy images and videos solely using preoperative CT images to address these unmet needs. We train a 3D UNet model to automatically segment CT images and construct 3D surfaces. These surfaces are then skeletonized for rendering and camera position tracking. Finally, we train a style transfer model using Contrastive Unpaired Translation (CUT) to synthesize realistic ureteroscopy images. Cross validation on the UNet model achieved a Dice score of 0.853 $\pm$ 0.084 for the CT segmentation step. CUT style transfer produced visually plausible images; the Kernel Inception Distance to real ureteroscopy images was reduced from 0.198 (rendered) to 0.089 (synthesized). We also qualitatively demonstrate the entire pipeline from CT to synthesized ureteroscopy. The proposed ASSIST-U system shows promise for aiding surgeons in visualization of kidney ureteroscopy.

This letter considers the problem of beamforming in multiple-input multiple-output (MIMO) radar. The mismatch phenomenon of MIMO radar virtual array steering vector is addressed and a new robust beamforming method for MIMO radar is proposed. The object function of this robust MIMO radar beamformer is constructed from an infinite norm of the output data, which is solved by linear programming. The performance of the proposed beamformer is verified by simulation results. Numerical results illustrate that proposed beamformer exhibits good performance improvement in virtual array steering vector mismatch compared to conventional methods.

As electric vehicles(EVs) begin to participate in the peak regulating auxiliary service market, it has become a major problem that how can price aggregators maximize the peak shaving capacity provided by EVs and maximize their own interests. This paper proposes a bargaining game pricing method based on the psychology cost of EVs and the risk assessment of aggregators. First of all, according to the impact of users’ participation in peak shaving on the battery life of EVs, the impact of participation in peak shaving on users’ original travel plans and time, and the impact of aggregator pricing on users’ psychology, the comprehensive psychology cost of EV users is obtained. Then, based on the user’s psychology cost and the law of gravitation, the evaluation scheme for the peak shaving capacity of EVs is obtained. On the basis of conditional value at risk(CVaR), the mixed CVaR is obtained by considering the behavior of users who may chase risks. Based on the mixed CVaR, the risk assessment of aggregators’ participation in the peak regulating auxiliary service market is carried out. According to the above information, the aggregators and the EV teams are engaged in a bargaining game based on the peak shaving pricing problem, which is divided into complete information game and incomplete information game. Finally, the feasibility of the proposed method is verified by an example analysis.

We propose criteria for recess etching to fabricate T-gate used in InGaAs HEMTs. By patterning additional rectangular pads on the source and drain metals in the e-beam lithography step, it is possible to measure the drain-to-source resistance (Rds) and current (Ids). the ratio (Γ) of before and after etching for each Rds and Ids can be used as criteria to determine the point in time to stop etching. By performing recess etching with Γ= 1.97 for Rds and Γ= 0.38 for Ids on an epiwafer having cap doping concentration of 2= 1019 cm−3 and channel indium content of 0.7, we have fabricated InGaAs mHEMT device showing gm,max= 1603 mS/mm and ft= 290 GHz at Lg= 124 nm. The criteria presented can be applied to InGaAs HEMTs with various epitaxial structures.

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.

This letter proposes a cross-domain WiFi-based gesture recognition system (WiCross) based on a dynamically weighted multi-label generative adversarial network. Most existing WiFi-based gesture recognition systems are user, orientation, and environment sensitive, which limits the application of WiFi sensing. Compared with the influence of users and environments on WiFi sensing systems, the influence of orientation on WiFi sensing systems is more difficult to remove. To alleviate the confusion caused by the orientation more effectively, we arrange the transmitting and receiving antennas according to the characteristics of the Fresnel region. We propose to dynamically weight different links according to users’ orientations and use a multi-label generative adversarial network to obtain domain-independent features. More importantly, WiCross can use domain-independent features to classify some unknown gestures without modifying any code or data set. Lightweight computing resource consumption allows WiCross to respond in real-time. The experimental results show that WiCross can achieve an in-domain recognition accuracy of 93.54% and a cross-domain recognition accuracy of 93.11%

Identifying cohesive subgraphs is an important topic in graph theory and complex network analysis. The quasi-clique, as a generalization of clique, can be used to identify functional and structural properties of various networks. In this paper, we study the maximum weighted quasi-clique problem, and propose a local search algorithm for solving the problem. In the algorithm, an iterated local search method is used as the search framework. To find the quasi-clique with the maximum total weights, hybrid vertex selection strategies are proposed and incorporated into our algorithm. The hybrid strategies utilize a probability-based mechanism for choosing sub-strategies in each round of the local search. We conduct experiments on synthetic networks and real-world networks to show the effectiveness of our algorithm. The results indicate that hybrid strategies perform better than existing methods, and thus our algorithm has a good ability to tackle various networks.