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Abstract A rotor speed signature analysis‐ (RSSA‐)based inter‐turn short circuit (ITSC) fault diagnostic method is proposed, which is robust with regard to speed‐ and current‐loop bandwidths of permanent magnet synchronous machine (PMSM) drive systems. Conventional ITSC fault detection solutions that rely on current signals or extra devices. Thus, an initial step of investigation on the validity of RSSA is taken for residual insulation capacity monitoring of the ITSC fault at the incipient stage. The Vold–Kalman filtering order tracking method is employed for the real‐time extraction of fault features. Besides, the impact of speed‐ and current‐loop controller bandwidths on ITSC fault diagnosis is also analysed and an Adaline estimator is designed to decouple their impacts. Finally, the effectiveness of the proposed method is verified on a faulty PMSM, which exhibits satisfying results in tracking the degradation of residual insulation, even if the phase currents are significantly distorted due to low switching frequency of the inverter.

The low-carbon agenda promotes the increasing penetration of renewable energy, this however also results in a rapid decrease in the flexibility of power systems with less controllable generation. There is an urgent requirement to explore user-side resources to provide flexibility for the power system. The regional integrated energy system (RIES) contains a large number of multi-energy coupling devices with abundant flexibility. However, considering the multi-energy complementary characteristics and complex multi-energy networks, as well as the uncertainty of the renewable energy, it is difficult to quantify the energy purchasing demand and flexibility support ability of RIES in the market, making it challenging to identify the RIES feasible region. This paper proposes a novel approach to tackle this challenge by introducing a projection method based on second-order cone programming. The approach aims to describe the energy purchasing and flexible reserve feasible regions of an RIES. Specifically, a max–min RIES operating model is constructed with the goal of minimizing the distance from the boundary of the feasible region in the worst case. Subsequently, the strong duality principle and Karush-Kuhn-Tucker (KKT) optimality conditions are leveraged to reformulate the max–min optimization problem into a solvable second-order cone programming problem. Additionally, the chance constraint is introduced to address and express the risk associated the uncertainty of the renewable energy output in RIES. Moreover, for the convenience of subsequent solving, the deterministic equivalent method is used to convert it into a deterministic constraint. Case studies conducted on an RIES incorporating a 33-bus distribution network and a 7-node natural gas network demonstrate the effectiveness and efficiency of the proposed approach in projecting the feasible region in terms of both operating costs and computational time.

State-of-the-art image object detection computational models require an intensive parameter fine-tuning stage (using deep convolution network, etc). with tens or hundreds of training examples. In contrast, human intelligence can robustly learn a new concept from just a few instances (i.e., few-shot detection). The distinctive perception mechanisms between these two families of systems enlighten us to revisit classical handcraft local descriptors (e.g., SIFT, HOG, etc.) as well as non-parametric visual models, which innately require no learning/training phase. Herein, we claim that the inferior performance of these local descriptors mainly results from a lack of global structure sense. To address this issue, we refine local descriptors with spatial contextual attention of neighbor affinities and then embed the local descriptors into discriminative subspace guided by Kernel-InfoNCE loss. Differing from conventional quantization of local descriptors in high-dimensional feature space or isometric dimension reduction, we actually seek a brain-inspired few-shot feature representation for the object manifold, which combines data-independent primitive representation and semantic context learning and thus helps with generalization. The obtained embeddings as pattern vectors/tensors permit us an accelerated but non-parametric visual similarity computation as the decision rule for final detection. Our approach to few-shot object detection is nearly learning-free, and experiments on remote sensing imageries (approximate 2-D affine space) confirm the efficacy of our model.

The increasingly frequent extreme events pose a serious threat to the resilience of the power system. At the same time, the power grid is transforming into a new type of clean and low-carbon power system due to severe environmental issues. The system shows strong randomness with a high proportion of renewable energy, which has increased the difficulty of maintaining the safe and stable operation of the power system. Therefore, it is urgent to improve the resilience of the new power system. This paper first elaborates on the concept of power system resilience, listing the characteristics of new power systems and their impact on grid resilience. Secondly, the evaluation methods for resilient power grids are classified into two categories, and measures to improve the resilience of the new power system are reviewed from various stages of disasters. Then, the critical technologies for improving the resilience of the new power system are summarized. Finally, the prospective research directions for new power system resilience enhancement are expounded.

In order to develop continuously variable transmission (CVT) suited for electric vehicle (EV), a comparative analysis of the transmission used on EV is introduced in this paper. To solve the problems of traditional CVT used for EV, a newly designed electric CVT (ECVT) is proposed. The final drive ratio, speed ratio range of variator and hydraulic system of ECVT are redesigned for improving the transmission efficiency. Considering that the permanent magnet synchronization traction motor (PMSTM) of EV has an ideal driving characteristic (Constant torque at low speed, constant power at high speed) and wider high efficiency area compared with gasoline engine, the speed ratio control strategy of ECVT is studied again for improving the vehicle driving range and better meeting the requirements of driver. Finally, a simulation test system is fabricated for evaluating the battery energy consumption of an EV equipped with ECVT and single-speed transmission (SST) under constant speed and road drive cycle. The simulation results show that the ECVT proposed can extend the endurance mileage of EV at the speed of 20 to 90km/h, and improve the acceleration performance from 0 to 50km/h. The EV equipped with ECVT showed more advantages compared with SST in urban conditions.

Abstract This paper studies a distributed formation problem for non‐holonomic mobile robots. Consideration of the leader dynamics of the robots as non‐ideal, that is, subject to disturbances/unmodelled variables, is the distinguishing feature of this work. The issue is resolved by a distributed combined disturbance‐and‐leader estimator, allowing for the distributed reconstruction of the leader's signals. The estimator needs to detect the leader's information and disturbance. In order to reject such disturbance and achieve the formation asymptotically, the control law incorporates the smooth estimator's estimate of the leader disturbance. Furthermore, the stability of the total distributed formation control algorithm is also examined using the Lyapunov technique. Finally, to show the viability of the proposed theoretical results, simulations and actual experiments are carried out.

Hyperspectral anomaly detection aims to fast and credibly find nontrivial candidate targets without prior knowledge, which has become an increasingly pressing need as imagery swath and resolution are growing rapidly. Relevant state-of-the-art learning-based anomaly detection approaches have benefited from data-driven hierarchical feature embeddings that typically model the geometric distribution of spectral vectors. However, most of these techniques are incompatible with resource-constrained applications: 1) huge computational costs caused by feedforward convolution operation cannot be supported with limited computation resources and storage (e.g., in-orbit processing); 2) detection accuracy relies on large-scale training datasets, which results in labor-expensive imagery collection, pixel-level labeling, and time-consuming learning procedure. To address these issues, we advocate a learning-free, frequency domain anomaly detection method combined with predictive coding, an intriguing heuristic prior from human vision system. Technically, 1) inherently efficient frequency transformation could be implemented with existing image compression modules (e.g., JPEG or JPEG2000 codec), which improves the utilization of computational resources; 2) predictive coding mechanism is exploited for suppressing frequently occurring information represented in the low-entropy frequency domain, such that the “unpredictive” subject (i.e., anomaly spectrums) can “pop out” with naive residuals. Such heuristic prior incorporated into the computational model can reduce dependence on the large-scale training set. Experiments on real-world hyperspectral datasets confirm the efficacy of our model. Besides, low computation cost of the proposed (fast frequency transformation and analytical solutions) anomaly detector facilitates rather straightforward sliding window verification in high-resolution imagery.

In response to increasing environmental deterioration, the vigorous development of the integrated energy system is an important measure to achieve the goal of carbon neutrality. In order to ensure that the system takes into account the economic operation under the premise of low carbon and environmental protection, this paper proposes a bi-level optimal low-carbon economic operation model for the regional integrated energy system (RIES). At the upper level, the objective of the RIES is economic optimization, which contains the carbon emission cost so that the system would change its preference for high-carbon energy to limit the carbon emission of the system. At the lower level, an electricity and natural gas pricing model is established, and a carbon emission flow (CEF) model is used to calculate the price of carbon emissions. This proposed bi-level optimization model is converted to a single-level mathematical problem with KKT (Karush–Kuhn–Tucker) conditions for efficient calculation. The proposed optimal model is tested on the five-bus power system and seven-node natural gas system, and numerical results indicate the optimal operating model with this proposed carbon pricing method can effectively reduce carbon emissions and minimize the total operating cost of RIES.

Modern neural networks [e.g., Deep Neural Networks (DNNs)] have recently gained increasing attention for visible image classification tasks. Their success mainly results from capabilities in learning a complex feature mapping of inputs (i.e., feature representation) that carries images manifold structure relevant to the task. Despite the current popularity of these techniques, they are training-costly with Back-propagation (BP) based iteration rules. Here, we advocate a lightweight feature representation framework termed as Guided Random Projection (GRP), which is closely related to the classical random neural networks and randomization-based kernel machines. Specifically, we present an efficient optimization method that explicitly learns the distribution of random hidden weights instead of time-consuming fine-tuning or task-independent randomization configurations. Further, we also report the detailed mechanisms of the GRP with subspace theories. Experiments were conducted on visible image classification benchmarks to evaluate our claims. It shows that the proposed method achieves reasonable accuracy improvement (more than 2%) with moderate training cost (seconds level) compared with other randomization methods.

Nonideal switching characteristics of voltage source inverter (VSI) will result in distortions in both phase currents and reference voltages of a permanent magnet synchronous machine (PMSM) drive system, which will also consequently introduce ripples into the output speed and shaft torque, and result in a performance deterioration in the state observation control. To solve this issue, a method of directly extracting and compensating the nonlinear voltage disturbance due to VSI nonlinearity employed in PMSM drives is proposed in this paper. Firstly, the distorted voltage due to VSI nonlinearity is directly extracted via a harmonic separation scheme, which can be easily compensated by a proportional integral controller afterwards. Since both 6th harmonic and DC terms of dq-axis distorted voltages due to VSI nonlinearity are well compensated, the proposed method shows sufficiently good performance under both $i_{d}=0$ and maximum torque per ampere controls ( $i_{d} < 0$ ), as verified on two prototype PMSMs being salient pole and non-salient pole, respectively, both developed for a low-speed rotary actuator. In addition, the application of proposed method to state observation controls such as the model-based sensorless control and the deadbeat predictive current control, is finally investigated. Test results manifest that the proposed method can effectively improve the accuracy of observed rotor position/speed, and the ability of current tracking.

Emotion analysis has emerged as one of the most prominent study areas in the field of Brain Computer Interface (BCI) due to the critical role that the human brain plays in the creation of human emotions. In this study, a Multi-objective Immunogenetic Community Division Algorithm Based on Memetic Framework (MFMICD) was suggested to study different emotions from the perspective of brain networks. To improve convergence and accuracy, MFMICD incorporates the unique immunity operator based on the traditional genetic algorithm and combines it with the taboo search algorithm. Based on this approach, we examined how the structure of people’s brain networks alters in response to different emotions using the electroencephalographic emotion database. The findings revealed that, in positive emotional states, more brain regions are engaged in emotion dominance, the information exchange between local modules is more frequent, and various emotions cause more varied patterns of brain area interactions than in negative brain states. A brief analysis of the connections between different emotions and brain regions shows that MFMICD is reliable in dividing emotional brain functional networks into communities.

Background: To investigate the relationship of circulating tumor cells (CTCs) and the clinical characteristic parameters and prognosis in patients with head and neck squamous cell carcinoma (HNSCC). Methods: The retrospective clinical study included 95 patients with HNSCC who after surgery in Shanghai Ninth People’s Hospital affiliated to Shanghai Jiao Tong University School of Medicine between December 2015 and December 2016. All patients were followed up for survival until the end of June 2019. The CTCs detection was performed by negative enrichment (NE) immunofluorescence-in situ hybridization (im-FISH) of chromosome 8. Results: Patients with higher CTCs counts are associated with a worse prognosis with an area under the receiver operator characteristic (ROC) curve of 0.756 [95% confidence interval (CI) 0.640-0.872, p = 0.001]. The CTCs-positive rate of HNSCC patients was 58.9% (56/95) by using the cut-point of 3. Both the chi-square test and binary logistic regression analysis showed that the N stage and clinical stage were significantly associated with CTCs-positive in patients with HNSCC ( p < 0.05). Further Non-parametric test analysis indicated that more CTCs counts were detected in late N and clinical stages patients ( p < 0.001). The Kaplan-Meier survival analysis indicated that CTCs-positive were correlated with shorter progression-free survival (PFS) ( p < 0.001) and overall survival (OS) ( p = 0.001). Further, the CTCs-positive was an independent prognostic factor for PFS and OS according to the Cox multivariate regression analysis ( p < 0.05). Conclusion: More CTCs were associated with N stage, clinical stage, poor prognosis in patients with HNSCC, which might be used as a prognostic biomarker.

A method for real-time estimation of rotor flux linkage of permanent-magnet synchronous machines (PMSMs) under both steady state and free-running conditions is proposed in this paper. At steady state, a method for the estimation of rotor flux linkage is proposed based on the injection of variable-period zero-voltage perturbation, of which the accuracy is irrespective of the influence of voltage-source inverter (VSI) nonlinearity. Moreover, for the estimation of rotor flux linkage under free-running condition, due to system inertia after shutdown or fault in the motor driver, an effective approach using history data recorded at different transients of rotor speeds is developed, which has eliminated the influence of VSI nonlinearity during the modeling process. The proposed two methods are experimentally validated on a down-sized PMSM prototyped for electric vehicle application, which shows good performance for the estimation of rotor flux linkage under both steady state and free-running conditions.

Understory vegetation plays a crucial role in nutrient turnover and cycling in plantations, but it also competes for nutrients with crop trees when only a single species is present due to its specific nutrient requirements. However, it remains unclear whether this competition can be alleviated when the species richness of understory vegetation increases. In this study, we tested different gradients of understory vegetation species richness, including understory vegetation removal (UR), the retention of a single main understory vegetation species (RS), and the retention of natural diverse understory vegetation (RD) as part of a poplar (Populus deltoides ‘Nanlin-3804′) plantation, to study their effects on poplar growth, and to evaluate nitrogen (N) usage and how this was affected by the interactions between the poplar and understory vegetation. The results showed a generally lower periodic growth, and a significant decline in the foliar chlorophyll content and glutamine synthetase activity of poplar under treatment with RS and RD compared to those under UR treatment conducted in July 2019, which clearly indicated N competition between the understory vegetation and poplar trees. However, no significant difference was detected in the foliar chlorophyll content and glutamine synthetase activity of poplar under RD and RS treatment; only the nitrate reductase activity in poplar leaves under RD treatment declined significantly, by 22.25%, in June 2019. On the contrary, the diameter at breast height (DBH) of the poplar under RD treatment showed an increase of 34.69% from July to August 2019, compared with that under RS treatment. Furthermore, the increase in the species richness of understory vegetation resulted in an increase in the δ15N values in the poplar leaves, which was strongly regulated by the NH4+-N pool in the 10–20 cm soil layer, indicating the effective coordination of N utilization between poplar and understory vegetation when diversified understory plant species were present. These findings demonstrate the essential role of understory vegetation species diversity in alleviating N competition with crop trees, and provide guidance for understory vegetation management in poplar plantations.

In this work, the fatigue behavior of a low-cost Zr58Cu15.46Ni12.74Al10.34Nb2.76Y0.5 (at%) bulk metallic glass (BMG) fabricated by industrial-grade Zirconium raw material was investigated under three-point bending loading mode. X-ray, fatigue tests under different stress amplitude and fatigue fractography were conducted in order to characterize the amorphous structure, fatigue stress-life (S-N) curve and fracture mechanism, respectively. It is found that the X-ray diffraction (XRD) result showed a fully amorphous structure due to high glass-forming ability, cracks initiated from inclusions near the rectangular corners at tensile surfaces and the fatigue endurance limit (~168 MPa) and fatigue ratio (~0.13) termed as fatigue endurance limit divided by ultimate tensile strength in stress amplitude were comparable to the similar BMG prepared by high pure raw materials.

A thermal-induced measurement error induced by thermal deformation of digital camera in photo mechanics methods that has the same magnitude of the error as that in the resistance strain measurement method reduces the accuracy of high-precision measurement substantially and must, therefore, be compensated. Starting from the underlying mechanism of a thermal-induced measurement error, we investigated the image error introduced by thermal behaviours of digital cameras widely used in photo mechanics. We experimentally determined the relationships between the thermal behaviours and temperature, derived the relationship between the image error and measurement error of photo mechanics methods, and eventually established a physical model to explain the underlying relationship between the thermal-induced measurement error and temperature. Furthermore, based on the investigation of the underlying mechanism, we introduced three types of temperature compensation methods for photo mechanics, namely the model compensation method, the preheating method, and the compensation specimen method. We experimentally demonstrated the feasibility of these compensation methods. The model compensation method only needs the data regarding the environmental temperature during operation of the digital camera to implement the correction of the measurement results and enhance the measurement accuracy of photo mechanics methods. The preheating method is suitable for indoor photo mechanics measurements wherein the environmental temperature is almost constant, which ensures that the appearance of thermal balance of the digital camera after a period of self-heating. The compensation specimen method reduces the effective resolution of the images and it also requires that the image error forms in the region of tested specimen are the same as that in the region of the compensation specimen.

Digital cameras represented by industrial cameras are widely used as image acquisition sensors in the field of image-based mechanics measurement, and their thermal effect inevitably induces thermal-induced errors of the mechanics measurement. To deeply understand the errors, the research for digital camera’s thermal effect is necessary. This study systematically investigated the heat transfer processes and temperature characteristics of a working digital camera. Concretely, based on the temperature distribution of a typical working digital camera, the heat transfer of the working digital camera was investigated, and a model describing the temperature variation and distribution was presented and verified experimentally. With this model, the thermal equilibrium time and thermal equilibrium temperature of the camera system were calculated. Then, the influences of thermal parameters of digital camera and environmental temperature on the temperature characteristics of working digital camera were simulated and experimentally investigated. The theory analysis and experimental results demonstrate that the presented model can accurately describe the temperature characteristics and further calculate the thermal equilibrium state of working digital camera, all of which contribute to guiding mechanics measurement and thermal design based on such camera sensors.

In this study, the pressure compensation mechanism of a reducer bellows is analyzed. This device is typically used to reduce the size of undersea instruments and improve related pressure resistance and sealing capabilities. Here, its axial stiffness is studied through a multi-fold approach based on theory, simulations and experiments. The results indicate that the mechanical strength of the reducer bellows, together with the oil volume and temperature are the main factors influencing its performances. In particular, the wall thickness, wave number, middle distance, and wave height are the most influential parameters. For a certain type of reducer bellows, the compensation capacity attains a maximum when the wave number ratio is between 6:6 and 8:4, the wall thickness is 0.3 mm, and the wave height is between 4-5 mm and 5-6 mm. Moreover, the maximum allowable ambient pressure of the optimized reducer bellows can reach 62.6 MPa without failure, and the maximum working water depth is 6284 m. [ABSTRACT FROM AUTHOR]