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Conventionally, the 2-parameter Weibull model, Arrhenius-Weibull model, has been used vastly for transformer aging-dependent unavailability modeling. However, this model only uses the lifetime feature to describe the transformer's degradation process and to calibrate the Weibull parameters, which harms the accuracy of aging-dependent unavailability forecasting. In response, this paper develops a 3-calibratable-parameter Weibull model for evaluating the transformer aging-dependent unavailability. In the proposed model, both the individualized aging threshold and lifetime are taken into the calibration of the Weibull parameters to accurately characterize the heterogeneity in transformer populations. First, a degree of polymerization (DP) analysis and Monte Carlo Simulation (MCS) based approach is proposed for estimating the transformers’ uncertain aging thresholds and lifetimes. Then, the Maximum Likelihood Estimate and Particle Swarm Optimization are jointly adopted to model the relationship among the calibratable Weibull parameters, aging threshold, and lifetime. Finally, the analytical formula of aging-dependent unavailability is derived from the established 3-calibratable-parameter Weibull model using an integral-discretization method. A real utility application example in China's Chongqing power system has been presented to validate and demonstrate the practicality and usefulness of this method. [ABSTRACT FROM AUTHOR]

The papers in this special section focus on modeling and simulation in disaster and emergency management. The COVID-19 pandemic of 2020 has wrought damage globally to a degree that is totally unprecedented in human history.Within three months that the first known death from the illness caused by the so-called novel Coronavirus was reported by state media in China on the 11th of January 2020, close to 1.8 million people worldwide had tested positive, with more than 100,000 deaths reported. By the time the pandemic will have been declared to be under control, millions more are expected to have been infected and tens (possibly hundreds) of thousands more will have perished as a result of the disease. Not only are the numbers of COVID-19 infected people and deaths staggering. Economies throughout the world have suffered tremendously, with many companies forced to shut down, or at the very least significantly scale down, their production of goods and services as a result of the need for social distancing and self-isolation required to prevent the unmitigated transmission of the disease. All this has led to hundreds of millions worldwide losing their employment or other sources of livelihood. Governments of all nations, large and small, developed and underdeveloped, have needed to pump into their economy and onto their struggling citizens whatever financial resources they could manage to generate. [ABSTRACT FROM AUTHOR]

The online retail market shows a sharp increase in traffic during holiday sales. The ability to distinguish customers who will likely purchase is critical for provisioning traffic and for providing cost-effective promotions. This paper uniquely studies the browsing and purchasing behaviors of online shoppers during a yearly sale event in China, the world’s largest online marketplace. Based on 31 million action logs gathered from wide residential areas, we characterize the steps leading to purchases and determine their precursors. We investigate the effect of time (e.g., date, time of date), environment (e.g., platform, viewed category), and action (e.g., session time, clicks, sequence) on purchases. Action cues from shopping behaviors can be used for early detection. While most shoppers start with strong intentions to purchase, yet the moment of ordering comes rather impulsively within 30 seconds to several minutes of browsing. The predictive accuracy reaches as a high AUC of 0.924. The findings in this paper provide an understanding of traffic during mega sale events that can help online shops plan and provide a better user experience for upcoming shopping festivals. [ABSTRACT FROM AUTHOR]

With the proliferation of online social networks, the identification or classification of real-life relationship between users has been very useful for many applications such as financial fraud detection. In real life, usually people with different relationships would present gifts with special meanings to each other on different dates. In many Asian cultures, especially in Chinese culture, the red packet is a traditional form of monetary gift. With the rapid development of the Internet, people gradually began to give electronic red packets instead of paper ones as the means of money gifting on social network platforms. As motivated, in this paper we advocate a novel approach that exploits users’ red packet interactions for users relationship identification on WeChat, one of the largest social platforms in China. Specifically, we analyze the WeChat red packets network, identify the real-life relationship types between users through mining the semantic information of the amount and sending time of each red packet. In order to better capture the red packet gifting behaviors between users for relationship identification, on one hand, we construct an Amount-Date Graph and apply the graph embedding method to learn embeddings of the amount and sending date of each red packet. On the other hand, we propose a novel sequential model, Cross & Attention Sequence Model (CASM), which explicitly learns the interactions between the latent semantic information of each red packet’s amount and sending date in the red packets sequence between two users. To validate our approach, we conduct comprehensive experiments on a real-world WeChat Users Red Packets dataset that involves 8 kinds of real-life relationships. The experiments show that our proposed approach performs significantly better than baselines and achieves 81.70 percent prediction accuracy. [ABSTRACT FROM AUTHOR]

The hybrid LCC/VSC cascaded HVDC (HC-HVDC) scheme, in which the LCC is adopted as rectifier, and one LCC in series with multiple paralleled VSC based MMCs is adopted as inverter, combines the advantages of both LCC and VSC-MMC technologies. Presently, a HC-HVDC project is planned to deliver the hydro power from Baihetan to Jiangsu in China. One of the major challenges is to suppress the DC overcurrent issue of VSC-MMCs especially under commutation failure condition of LCC at inverter side. Based on Baihetan-Jiangsu project under construction, the model of HC-HVDC system is developed in PSCAD/EMTDC. In the system, the rectifier employs a dual 12-pulse LCC, while the inverter adopts a hybrid scheme, i.e., one LCC converter in series with three paralleled VSC-MMC converters. This paper addresses the main cause and feature of the DC overcurrent, then proposes an overcurrent suppression control method for HC-HVDC system based on fuzzy clustering and identification approach. And the system transient performances with the proposed control approach are evaluated under AC fault conditions. The results show that the proposed approach can not only suppress the DC overcurrent following the commutation failure to a certain extent, but also improve the post-fault recovery performance of the overall system. [ABSTRACT FROM AUTHOR]

As part of the ongoing China Fusion Engineering Test Reactor (CFETR) studies, this work focused on the study of the electromagnetic (EM) loads distribution on inboard (IB) and outboard (OB) blanket vertical segments. In this paper, a major central disruption scenario with a 36-ms linear current quench was performed in the ANSYS finite element software. EM forces and moments, both on the single module and the complete blanket segment, were calculated for the updated helium-cooled ceramic breeder (HCCB) blanket. Furthermore, the effect of EM loads with respect to poloidal segmentations on the back plate (BP) was studied. Results show that the EM forces and moments in the IB blanket barely changed, but in the OB blanket, great changes occurred. With poloidal segmentations increasing, EM forces loads on the BP will be reduced. This paper may provide important data to support subsequent optimization of the BP of CFETR and could also be a technical reference for other fusion organizations. [ABSTRACT FROM AUTHOR]

The advances of high-frequency vacuum electron devices (VEDs) in China are reviewed in this paper, including traveling-wave tubes (TWTs) based on novel slow-wave structures (SWSs), high harmonic TWT amplifier, gyrotrons, Smith–Purcell (SP) radiation sources, surface plasmon polaritons (SPPs) sources, and terahertz (THz) radiation sources in ion-focused beam-plasma system. As one of the most widely used VEDs, TWTs based on folded type SWSs, planar SWSs, and grating type SWSs are studied theoretically and experimentally from Ka-band to R-band. Using folded type SWSs, two TWT amplifiers are developed. As high-power millimeter wave and THz wave sources, gyrotron TWTs, gyrotron oscillators, and gyroklystrons are presented. Moreover, this paper shows SP radiation sources, SPPs sources, and THz radiation sources in ion-focused beam-plasma system open good prospects for developing high-frequency VEDs with new mechanisms, which undoubtedly brings about innovative changes for the development of VEDs. [ABSTRACT FROM AUTHOR]

In the coronavirus epidemic, many Chinese hospitals have established buffer zones to prevent the spread and transmission of the virus. The buffer zone is a monitored and separate area where the patients who need hospitalizations after the quick treatments in the emergency department can temporarily wait for the Covid-19 test and receive some healthcare services to stabilize their conditions. Because the beds in the buffer zones are limited, the managers face the patient admission control problem for the buffer zone. This management and control problem is challenging since the patient arrivals are uncertain, and the patients’ conditions are different. In this paper, we build the infinite- and finite-horizon Markov decision process (MDP) models for this problem. We use the uniformization method to discretize the patient flow. We propose various iteration algorithms to solve the MDP models and obtain the optimal and threshold policies. Numerical experiments validate the advantages of the policies obtained by the algorithms in this paper over the current policies of hospitals. Note to Practitioners—The ongoing COVID-19 pandemic has been causing enormous damage to people’s health, jobs, and well-being. COVID-19 has affected almost all countries globally and has changed the operation mode of the healthcare system, especially the hospitals. The hospitals are the frontlines of healthcare service and the battle with the COVID-19 pandemic. This article is motivated by our collaborations with hospitals in Shanghai, China. In China, many hospitals establish buffer zones: a monitored area where the patients who need hospitalizations after the quick treatments in the emergency department can temporarily wait for the Covid-19 test and receive some healthcare services to stabilize their conditions. Because the zone’s capacity is limited, the managers must make dynamic patient admission control decisions according to multiple factors, such as patients’ health conditions and the usage of beds in the zone. We propose two MDP models to solve this complex problem. Several iteration algorithms are designed to solve the MDP models and obtain the optimal and threshold policies. Based on hospitals’ real-life data, we show the methods presented in this paper can help hospital managers make more reasonable decisions. Although we focus on the hospital’s buffer zone in China, the methodology and approach for this problem can be extended to other practical hospital management scenarios in the coronavirus pandemic. For example, For example, some hospitals have admission control problems for coronavirus patients due to hospital capacity limitations. The hospital has to decide if a patient is accepted as an inpatient or suggested to home quarantine. In such a case, the admission control problem can also be solved by the methodologies in the paper. [ABSTRACT FROM AUTHOR]

The radio block center (RBC) is one of the most essential ground systems in a high-speed train control system both in Europe and in China. The RBC handover procedure is an important function of RBC, which affects the transport efficiency, reliability and safety of railways. Analysis of crucial states in the RBC handover procedure is helpful to determine whether there are potential risks in the procedure, and to locate the fault in time when a fault occurs. In this paper, we study a property, called C-detectability, of the RBC handover. This property has been defined in discrete event systems and requires that the crucial states can be determined uniquely by observing the system output. Taking the RBC handover procedure in the Chinese train control system level 3 (CTCS-3) as an example, we first model the RBC handover procedure using Petri nets, which are a graphical and mathematical modeling tool to formalize the behavior of discrete event systems. Then, based on the notion of basis reachability graph, an efficient approach is used to check C-detectability of the Petri net modeling the handover procedure. Note to Practitioners—The railway system is a safety-critical system. System safety is essential to the railway system. It is necessary to provide formal methods to model the system and analyze its properties. The motivation of this work is to present a general modeling framework of railway systems for the crucial states estimation. The crucial states estimation of the railway system has not been discussed yet in the literature despite its importance due to its relationship with the safety of the railway. In particular, estimating the crucial states in the railway system helps to determine whether there are potential risks in the system. In this paper, we investigate a new state estimation property (C-detectability), which is related to fault diagnosis, marking estimation and fault identification. Future research will consider the use of timed Petri nets to increase the modeling power and to allow the performance analysis of the railway system. [ABSTRACT FROM AUTHOR]

This paper investigates the ownership and location strategies as contingency factors, which affect the relationship between SMEs’ open innovation activities and innovation performance in the context of emerging economies. Drawing from the corporate strategy and open innovation literature, we argued that SMEs with state and foreign ownership can more effectively capture economic rents from open innovation in a weak intellectual-property-rights regime. Meanwhile, the subnational environments in which SMEs locate their businesses is also crucial for them to capture value from open innovation. Using the data of 1050 SMEs in China, we found that state ownership positively and significantly moderates the relationship between openness and innovative performance, while foreign ownership partially supports the same effect. By disentangling the pro-business environment and the market development, we found that for SMEs that are located in a region with a stronger pro-business environment and a higher level of market development, open innovation can bring more value than it does for those located in regions with lower levels of those factors. Overall, this paper also contributes to our understanding of the complementary relationships between ownership and location strategies and open innovation. [ABSTRACT FROM AUTHOR]

Quality inspection, a widely adopted practice in supply chains, measures whether delivered products conform to prespecified quality requirements. Due to potential economic benefits, suppliers may deliberately manipulate products to falsify test results. However, unqualified products could cause severe problems in supply chains or even tragedies, such as China’s tainted milk scandal. In this paper, we propose a belief-desire-intention modeling approach to predict suppliers’ behavior and provide inspection suggestions to buyers to overcome the problem. Assuming access to the production process, our approach can represent suppliers’ knowledge of production and deception to mimic their reasoning processes and predict their deception intentions. This flexible approach can also adapt to environmental changes and deliver effective results. In this paper, we build a prototype system for supply chain quality inspections based on the proposed method. We conduct laboratory experiments to collect data for computational assessments of the performance of the prototype. It is shown that our proposed approach is more accurate than classic machine learning methods in detecting suppliers’ deceptions. [ABSTRACT FROM AUTHOR]

Urban power grids are facing many operational and expansion challenges to meet further demand growth and increased reliability requirements. Advanced transmission technologies have been considered by the electric utilities to effectively increase the utilization of existing infrastructure and operational flexibility. The focus of this paper is on VSC-HVDC technologies for urban power grid enhancement and modernization. First, a potential technical scheme is proposed for converting an existing AC circuit to DC operation, which could boost the power transfer capability of the critical transmission corridor and increase network operational flexibility. Second, this paper proposes three operation modes for the VSC-HVDC interties in urban power grids corresponding to normal, emergency and island operating conditions, respectively. An integrated, adaptive emergency control strategy (AEC) is proposed that can enable adaptive power flow responses of the VSC-HVDC intertie under varying system operating conditions and critical contingencies. The flexibility and effectiveness of the proposed operational principles of urban VSC-HVDC intertie and the corresponding control strategies are verified in PSCAD/EMTDC using a realistic urban power grid in China. [ABSTRACT FROM AUTHOR]

High renewable energy penetration increases the electricity seasonal imbalance in the long-term timescale. Power system planning needs to consider the optimal configuration of various flexibility resources and electricity balance in different timescales. The coupling of multiple timescales largely increases the computation complexity of the power system planning problem. Thus, this paper presents an efficient source-grid-storage co-planning model which incorporates a year-round hourly operation simulation. To improve the computation efficiency of the planning model, from the temporal scale, a self-adaptive compact panorama time series (CPTS) model is applied, which greatly reduces the number of variables related to short-term decisions. From the spatial scale, a network-constrained relaxed clustered unit commitment (NC-RCUC) model is introduced, which significantly reduces the number of variables related to unit commitment decisions. Case studies on the modified Garver’s 6-node system and HRP-38 system prove the validation and efficiency of the proposed model (“HRP” stands for high renewable penetration). The studies on the China power grid in 2035 demonstrate the future planning results of generation, transmission and storage in China power systems based on the proposed model. [ABSTRACT FROM AUTHOR]

Osteosarcoma is the most common malignant osteosarcoma, and most developing countries face great challenges in the diagnosis due to the lack of medical resources. Magnetic resonance imaging (MRI) has always been an important tool for the detection of osteosarcoma, but it is a time-consuming and labor-intensive task for doctors to manually identify MRI images. It is highly subjective and prone to misdiagnosis. Existing computer-aided diagnosis methods of osteosarcoma MRI images focus only on accuracy, ignoring the lack of computing resources in developing countries. In addition, the large amount of redundant and noisy data generated during imaging should also be considered. To alleviate the inefficiency of osteosarcoma diagnosis faced by developing countries, this paper proposed an artificial intelligence multiprocessing scheme for pre-screening, noise reduction, and segmentation of osteosarcoma MRI images. For pre-screening, we propose the Slide Block Filter to remove useless images. Next, we introduced a fast non-local means algorithm using integral images to denoise noisy images. We then segmented the filtered and denoised MRI images using a U-shaped network (ETUNet) embedded with a transformer layer, which enhances the functionality and robustness of the traditional U-shaped architecture. Finally, we further optimized the segmented tumor boundaries using conditional random fields. This paper conducted experiments on more than 70,000 MRI images of osteosarcoma from three hospitals in China. The experimental results show that our proposed methods have good results and better performance in pre-screening, noise reduction, and segmentation. [ABSTRACT FROM AUTHOR]

Operational indices optimization is crucial for the global optimization in beneficiation processes. This paper presents a multitasking multiobjective evolutionary method to solve operational indices optimization, which involves a formulated multiobjective multifactorial operational indices optimization (MO-MFO) problem and the proposed multiobjective MFO algorithm for solving the established MO-MFO problem. The MO-MFO problem includes multiple level of accurate models of operational indices optimization, which are generated on the basis of a data set collected from production. Among the formulated models, the most accurate one is considered to be the original functions of the solved problem, while the remained models are the helper tasks to accelerate the optimization of the most accurate model. For the MFO algorithm, the assistant models are alternatively in multitasking environment with the accurate model to transfer their knowledge to the accurate model during optimization in order to enhance the convergence of the accurate model. Meanwhile, the recently proposed two-stage assortative mating strategy for a multiobjective MFO algorithm is applied to transfer knowledge among multitasking tasks. The proposed multitasking framework for operational indices optimization has conducted on 10 different production conditions of beneficiation. Simulation results demonstrate its effectiveness in addressing the operational indices optimization of beneficiation problem. Note to Practitioners—Operational indices optimization is a typical approach to achieve global production optimization by efficiently coordinating all the indices to improve the production indices. In this paper, a multiobjective multitasking framework is developed to address the operational indices optimization, which includes a multitasking multiobjective operational indices optimization problem formulation and a multitasking multiobjective evolutionary optimization to solve the above-formulated optimization problem. The proposed approach can achieve a solution set for the decision-making. The simulation results on a real beneficiation process in China with 10 operational conditions show that the proposed approach is able to obtain a superior solution set, which is associated with a higher grade and yield of the product. [ABSTRACT FROM AUTHOR]

Land surface temperature and emissivity (LST&E) are key variables for a wide variety of surface–atmosphere studies, and it is very important to validate the accuracy of different LST&E retrieval algorithms. In this paper, the accuracy of the Advanced Spaceborne Thermal Emission and Reflection (ASTER) temperature emissivity separation (TES) algorithm with the water vapor scaling (WVS) method and the ASTER standard LSE&E products (without WVS) was validated using 12 ASTER scenes from May 2012 to September 2012 with concurrent ground LST and emissivity measurements collected in an arid area in northwest China during the Heihe Watershed Allied Telemetry Experimental Research experiment. Both the National Center for Environmental Prediction (NCEP) and MOD07 atmospheric profile products were employed to perform the atmospheric correction. The results showed that the WVSTES LSTs retrieved from the two profiles both demonstrate good accuracies, with average biases of 0.34 and 0.24 K and average root-mean-square errors (RMSEs) of 1.52 and 1.46 K for the NCEP and MOD07 profiles, respectively. When the WVS was not applied, we obtained an average bias of 1.26 K and an average RMSE of 2.05 K for the AST08 product. The emissivities from the WVSTES algorithm with the two profiles both showed good agreements with the ground CE312 measurements, with mean differences of the five ASTER bands that were less than 0.01. AST05 showed anomalous emissivity spectra and underestimated the emissivity values of graybody surfaces when compared with the ground data, with mean differences of greater than 0.015, which were more obvious for cases with high water vapor. This paper demonstrated that the WVS method is critical for retrieving ASTER TES LST with accuracies within 1.5 K and emissivity within 0.015 for a wide range of atmospheric conditions and land surface types. [ABSTRACT FROM AUTHOR]

As a natural result of the electrified integration and electrical energy revolution, the electromagnetic launch (EML) technology will be inevitably used for future launch missions. This paper has summarized research focuses in this field according to the published papers on EML in China during the past decade and mainly introduced the current research and progress in several key technologies, including launcher, integrated launch package, high-power high-density supply, and overall control/maintenance. Some common pressing problems are pointed out to provide direction for the application of EML technology in engineering and many other fields, such as launcher life, miniaturization of power supply, and improvement of energy availability. [ABSTRACT FROM AUTHOR]

China is currently the world's largest producer and consumer of iron and steel. This industry is one of the most energy- and emission-intensive industries in China. It is unsuspicious that energy conservation and emission reduction in this industry are crucial for China to fulfill its obligations of reducing greenhouse gas emission and mitigating global climate change. An appropriate allocation of CO2 emissions permit is the most important precondition for establishing an efficient and effective emissions trading system that had recently been launched in China's pilot regions. This paper proposes a new emissions permit allocation method based on a Nash equilibrium data envelopment analysis model. This method is more appropriate in emissions permit allocation for China's iron and steel firms in an imperfectly competitive market where the product price is endogenous. A noncooperative game is formulated and the mixed complementarity problem is utilized to identify a Nash equilibrium in the allocation of emissions permit (AEP). The most up-to-date AEP using a unique data set of China's major iron and steel enterprises in 2014 is provided in this paper. The results show that the AEP improves the desirable output generation by satisfying the reduction targets of the undesirable outputs. [ABSTRACT FROM AUTHOR]

Voltage sag insurance (VSI) is an effective measure to reduce the voltage sag loss and risk for enterprises with insufficient mitigation confidence or economic ability. However, due to the lack of understanding of voltage sag risk (VSR), it is difficult for power utilities to identify the enterprises with VSI demands, and it is also unable to realize differentiated premium according to different enterprises' situations. To solve the above problems, this paper proposes an improved voltage sag premium model based on VSR. Firstly, the VSR factors are analyzed, such as the hazard, the vulnerability, the exposure. The process immune time is introduced to describe the vulnerability. Based on the traditional consideration of only the ability of users to resist voltage sag, the ability of users to recover from voltage sag is further considered. Then, the risk evaluation model of voltage sag is established. The evaluation results can help power utilities to judge whether enterprises have the demand of VSI. Finally, the improved premium model of VSI based on VSR is constructed to ensure the differentiation and rationalization of the premium. The correctness and rationality of this method are proved by ten manufacturing enterprises in China by analyzing the VSR and comparing the premium before and after the improvement. [ABSTRACT FROM AUTHOR]

This paper aims to understand the underlying mechanisms by which relational ties influence firm innovation. Companies in China rely much on relational ties to implement business strategies. Combining social network theory with dynamic capability perspective, we build a model that analyzes the direct and indirect impact of relational ties (business ties and service ties) on innovation performance through supply chain adaptability. We further analyze that technological turbulence and dysfunctional competition influence the relationships between relational ties and supply chain adaptability. We test these relationships using data collected from 300 manufacturers in China. The results show that service ties plays a relatively more important role than business ties in enhancing innovation performance directly. Interactions between business ties and service ties significantly influence innovation performance. Moreover, our results demonstrate that supply chain adaptability fully mediates the relationship between business ties and innovation performance but partially mediates the relationship between service ties and innovation performance. Our results show that technological turbulence positively moderates the relationships between business/service ties and supply chain adaptability. In addition, dysfunctional competition negatively moderates the relationship between business ties and supply chain adaptability. Overall, our findings contribute to the understanding of the roles of relational ties in supply chain. [ABSTRACT FROM AUTHOR]

Public–private partnership (PPP) is effective in delivering infrastructure projects. In China, PPP has been widely adopted in project finance ever since the government has permitted the private sector to get involved in the investment and operation of urban infrastructure. However, there seem to be many spurious PPP projects emerging in the market, which may bring about losses to the stakeholders and lead PPP away from orderly and standardized development. This paper attempts to address this issue by applying the fuzzy analytic hierarchy process to investigate two infrastructure PPP projects, and analyze the characteristics of genuine and spurious PPP projects in China. Recommendations for mass implementation of PPP and ways of identifying spurious PPP projects are summarized to guide the regulation of infrastructure PPP projects. The paper also provides a reference for project selection and policy-making in the future. [ABSTRACT FROM AUTHOR]

The high penetration of renewable energy will substantially change the power system operation. Traditionally, the annual operation of a power system can be represented by some typical operation modes and acts as the basis for the power-system-related analysis. The introduction of highly penetrated renewable energy will make the power system operation mode highly diversified and variable. These modes may not follow traditional empirical patterns. In this paper, we propose a data-driven method based on high-dimensional power system operation data (including power flow, unit generation, and load demand) to identify the pattern of the operation modes and analyze the impact of high renewable penetration. Specifically, the proposed data-driven method is composed of simulation, preprocessing, clustering, dimension reduction, and visualization with the aim to provide an intuitive understanding of the operation mode variety under high renewable penetration. In addition, several indices are introduced to quantify the space dispersion, time variation, and seasonal consistency of operation modes. A case study on actual Qinghai provincial power system in China validates the effectiveness of the proposed data-driven method and indicates that the dispersion and time variation of operation mode will significantly increase in the beginning and then saturate with the increase in renewable penetration level. The operation mode is also less correlated with seasons in renewable energy dominated power system. [ABSTRACT FROM AUTHOR]

Oscillations have been observed in wind farms with weak grid interconnections. While Texas observes 4 Hz low-frequency oscillations, the west region in China observes subsynchronous oscillations at 30 Hz. Furthermore, this oscillation mode caused torsional interactions with a remote synchronous generator and led to shutdown of the power plant. Inspired by those real-world events, this paper aims to present an analytical model of type-4 wind in weak grids that can demonstrate both low-frequency and subsynchronous frequency oscillations. Critical factors, e.g., the parameters of the phase-locked loop (PLL), are examined using small-signal analysis. The analysis results are validated using a testbed built in MATLAB/SimPowerSystems. Except power electronic switching sequences, the testbed has controls (e.g., wind turbine pitch control, maximum power point control, converter controls), machine dynamics and power system dynamics modeled. This testbed includes a 100 MW Type-4 wind farm, a 600 MW synchronous generator, a long transmission line and a grid. The testbed successfully demonstrates two types of dominant oscillations under different PLLs. In addition, the testbed demonstrates torsional interactions due to the proximity of the subsynchronous mode and one of the torsional modes. [ABSTRACT FROM AUTHOR]

The 4th International Conference on Radiation Effects of Electronic Devices (ICREED) was held on May 26–29, 2021, in Xi’an, China. The conference was held in-person and well attended. In addition, several virtual presentations were given by authors who were unable to attend due to the ongoing worldwide pandemic. Ten papers presented at ICREED are included in the May 2022 issue of the IEEE Transactions on Nuclear Science (TNS). Many of these papers highlight active research on radiation effects on microelectronics in China. Additional papers based on work presented at ICREED may appear in other issues of the Transactions. [ABSTRACT FROM AUTHOR]

Construction of the linearized state-space model is the key step of eigenvalue analysis for SSO study of power systems with large-scale renewable power generation (RPG) integrated. This paper proposes a novel method to construct the state matrix of the AC/DC hybrid power system with RPG within the discrete-time domain. The continuous-time state-space model of each AC/DC component as well as RPG units in the power system, is discretized together with their corresponding control. The discretized equation of each component is then represented by an equivalent circuit consisting of parallel historical current and conductance. According to the power system configuration, the discrete-time equivalent circuit of the whole AC/DC hybrid system is established, and the nodal analysis method is applied for the construction of the discrete-time state-space model of the power system. In this way, the state matrix can be acquired much more conveniently than that of continuous-time state-space method, which needs topology analysis to find independent state variables of the network from the proper tree. The proposed method has been applied in the eigenvalue analysis for the SSO study of a case system adapted from the AC/DC hybrid transmission system in northwest China. Eigenvalue results are in high accordance with time-domain simulation results in PSCAD/EMTDC. [ABSTRACT FROM AUTHOR]

While many machine learning approaches have been widely applied to power system online dynamic stability assessment, how to sufficiently learn spatial-temporal correlations from system transients without losing the interpretability is still a challenging issue. In this paper, a novel networked time series shapelet learning approach is proposed to learn spatial-temporal correlations for transient stability assessment (TSA) in an interpretable manner. Specifically, a network impedance-based adjacency matrix is first introduced to characterize spatial networked correlations. Based on graph structural regularization, this matrix is effectively incorporated into the subsequent learning procedure as spatial constraints. Taking time series trajectories acquired from multiple buses as the inputs, networked shapelet learning is heuristically performed to learn critical sequential features, i.e., networked shapelets, for TSA model derivation. With the learning procedure strategically guided by inherent spatial-temporal correlations of the system, the obtained data-driven TSA model is able to perform highly reliable and interpretable online TSA. Numerical test results on the IEEE 39-bus test system and the realistic GD Power Grid in China verify the superior performances of the proposed approach. [ABSTRACT FROM AUTHOR]

The development of new technologies in power generation and application, together with the demand for the reliability and capability of power supply has drawn increasing attention to dc distribution and microgrid technology. This paper introduces the renovation project for a single-circuit XLPE cable line being converted into dc operation after its second failure under 35 kV ac. This is the first time ever in China for this type of system conversion, in which the existing XLPE cable line was put into operation in a bipolar voltage-source converter (VSC) dc system of 10 kV voltage and 150 A current to maintain its 3 MW capacity. The test methods for cable performance that were checked under the designed dc scheme were put forward and conducted on sectioned cable samples. After the cable test and equipment installation, the line was put into operation under the VSC dc topology in 2011. It has now been running for four years without any interruption. Compared with other alternatives for ac operation, this proposal shows advantages in aspects of economy, reliability and engineering. Besides, the theoretical evaluation suggests that the system capacity can be further increased by four times, and the first step is underway for that goal. [ABSTRACT FROM PUBLISHER]

The uncertain nature of renewable energy resources (RERs) and fast demand response lead to recurring voltage violations in the power systems, which causes frequent transformer tap shifting and capacitor switching. Therefore, this paper resorts to the static var compensators (SVCs) to manage the bus voltages based on the multi-agent deep reinforcement learning (MA-DRL) algorithm. The proposed scheme includes several system agents and SVC agents to collaboratively adjust the injected reactive power to restrict the bus voltages within the normal range. All the agents are trained centrally and executed separately, which requires minimum communication cost. The IEEE 14-bus system, IEEE 300-bus system, and China 157-node urban power grid are used to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The rotational inertia and primary frequency response capabilities of power systems are declining as synchronous generators (SGs) are replaced by inverter-based variable renewable energy (VRE) resources. Therefore, when a high VRE proportion is present, it becomes essential to include frequency security constraints in power system planning. Under this circumstance, the decline in SGs would deteriorate the system frequency response performance; thus, inverter-based resources are supposed to provide frequency response support. This paper presents a frequency-constrained stochastic planning method for high VRE share systems. Frequency response support from wind farms is considered in the planning process for the first time, and the unique characteristics of wind power support are addressed. Specifically, the uncertainty of and variations in wind power support capabilities are modeled from the field-measured data, and the optimal combination of various support schemes can be determined by introducing binary variables with novel linear constraints into the planning process. Moreover, the nonlinear frequency constraints are transformed and embedded in the planning model by the proposed computationally efficient adaptive piecewise linearization method. The planning is formulated as a stochastic optimization model to consider the VRE power uncertainty. Finally, case studies on the IEEE 30-bus system validate the proposed method, with results indicating time savings of 22%. Further, compared with the case that only SGs provide frequency support, the results based on the East China system demonstrate that considering wind power support can improve the VRE share from 49.5% to 79.7%. [ABSTRACT FROM AUTHOR]

Though intercell scheduling problems have been studied in the literature, extant algorithms can hardly come into play in practice. This is because of two reasons: 1) transportation among cells, which is important for practical intercell scheduling, has not been adequately considered and 2) the problem size is large in practice, which may lead to intolerable computation efficiency. The motivation of this paper is to automatically design intercell scheduling heuristics that are suitable for practical application. A genetic programming algorithm with a pretraining strategy (GP-PS) is proposed. Production within cells and transportation among cells are simultaneously considered, and a cooperative coevolutionary framework is designed. To evolve better heuristics, a PS is developed. A speedup strategy is designed to accelerate the evolutionary process. Comparative experiments are conducted with other GP-based algorithms, speedup strategies, and with some state-of-the-art intercell scheduling algorithms. GP-PS is also put into use in a large manufacturing enterprise of China. Computational experiments and application results both verify the effectiveness of GP-PS. Note to Practitioners—Although intercell scheduling problems have been studied in the literature, it is still common to rely on manually generated schedules in industrial environments. This paper does not attempt to seek solutions directly, but try to automatically design good heuristic rules to address the problem, so that the computation efficiency that is required by real-time decision and the optimization performance that is important for a complex problem, can be satisfied at the same time. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of how to effectively smooth the short-term tie-line power fluctuations for grid-connected industrial microgrids (IMGs) from demand side. A wide-area closed-loop control architecture is proposed by directly and continuously modulating load power consumption, e.g., the aluminum electrolysis load. To optimally design the load smoothing controllers, an output regulation approach combined with LQ optimal control is developed. In particular, dominant frequencies of wind power fluctuations are analyzed and incorporated into the exogenous system that models time-varying disturbances. Simulations are conducted for a real industrial microgrid in the Inner Mongolia, China, to validate the practical feasibility and effectiveness of the proposed smoothing control strategy. It is demonstrated that the demand side control based on output regulation is able to achieve not only zero steady error in rejecting the time-varying wind power disturbance but also guarantee the satisfactory dynamic performance of modulated loads for the grid-connected IMG. [ABSTRACT FROM AUTHOR]

With the deepening of electricity market reform in China, the competition in the electricity retail market becomes increasingly intense. Electricity retailers (ERs) need to explore new business models to enhance their competitiveness in the retail market. Meanwhile, with the improvement of industrial production and people's living standards, more and more nonlinear electrical equipment have been put into use, leading to severe harmonic pollution problems. Harmonic pollution causes loss of electricity, resulting in the economic loss of customers, especially for large industrial customers. In the above contexts, this paper proposes a novel business model that incorporates harmonic control as a value-added service into electricity retail contracts for utility-owned ERs. Both utility-owned ERs and customers can benefit from the designed business model. For customers, it helps them to improve the power quality while saving the electricity cost. For ERs, it helps them to cultivate the customer loyalty and improve the customer satisfaction. A case study is performed to demonstrate the effectiveness of the proposed business model. [ABSTRACT FROM AUTHOR]

The Circular Electron Positron Collider (CEPC) is a proposing electron–positron collider in China, which aims for generating high center-of-mass energy colliding above 250 GeV. The high-granularity electromagnetic calorimeter (ECAL) is included in a calorimeter system, which is mainly responsible for the measurement of photon. A particle flow algorithm (PFA)-based ECAL solution will utilize about 400k channels/m3. Here, we present the design and development status of readout electronics based on SPIROC2b for the CEPC ECAL with high-granularity scintillants and silicon photomultipliers (SiPMs). The readout electronics consists of front-end electronics (FEE), data interface (DIF) board, and data acquisition (DAQ) board. The front-end board (FEB) carries SiPMs (S12571-010P), scintillants (PSD), and FEB application-specific integrated circuit (ASIC) (SPIROC2b). Two electronics were designed in this paper, one is for ASIC functionality and performance only, and the other is used as a single layer of a further prototype of ECAL. Electronic test and cosmic ray test have been done to electronics. The results indicate that a charge dynamic from 62 fC to 300 pC at S/N = 2 is successfully achieved, and the minimum ionizing particles (MIPs) are separated from the pedestal. [ABSTRACT FROM AUTHOR]

A long-term historical aerosol optical depth (AOD) data set from 1982 to 2011 over China (15–45° N; 75–135° E) with 0.1 spatial resolution has been produced from Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres—Extended level-2B data. The spatial distribution pattern shows that high AOD values are found in central and eastern China over the entire period with AODs larger in summer and spring than in autumn and winter. As the high-quality products from AERONET were absent for this period over mainland China, AOD data obtained using the broadband extinction method from solar radiation stations have been used to verify the quality of the AVHRR AOD data set over China. The intercomparison results show that the interannual variation of AOD has been well captured in the variation curve of the AOD monthly mean and the variation trend is also consistent over the whole period. The correlation coefficient of the monthly mean is mostly larger than 0.55, the agreement index is larger than 0.57, and the relative error is less than 21%. Both AVHRR and visibility data sets show high values in regions with rapid economic development. Using Moderate Resolution Imaging Spectroradiometer AOD data as references, it is found that AVHRR AOD from this paper has better accuracy in general than that from Deep Blue (DB) algorithm over China, especially over eastern and southern China, while DB provides more coverage especially over bright surface such as northwest China. This long-term historic AOD data set can be used together with other AOD data sets to study the climate and environmental changes, especially in the 1980s and 1990s. [ABSTRACT FROM AUTHOR]

This paper explores how to promote business model innovation through intraindustry search and extraindustry search with the assistance of fit dynamic capabilities (internal coordination capability and stakeholder engagement capability). Based on evolutional learning and business model literatures, six hypotheses are proposed and examined with data from 204 firms in China. The results show that intraindustry search has an inverted U-shaped effect on business model innovation, whereas extraindustry search has a positive effect. Internal coordination capability strengthens the effect of intraindustry search but weakens that of extraindustry search. Stakeholder engagement capability strengthens the effect of extraindustry search but weakens that of intraindustry search. Our findings enrich the business model literature by identifying different roles of intraindustry search and extraindustry search as the antecedents of business model innovation and their fitness with different types of dynamic capabilities. [ABSTRACT FROM AUTHOR]

Faults on the alternating current (AC) transmission lines could cause commutation failures of nearby line-commutated converter-based high-voltage direct-current (LCC-HVDC) systems, which in turn, may challenge the fault detection of AC systems. To address this issue, this paper proposes a fast fault detection method based on instantaneous active power and fault variation component to alleviate the impacts of commutation failures and reactive power compensation while mitigating the dead zone problem. Upon that, an integral-based differential protection algorithm is presented to enhance reliability. Simulations are carried out on a real hybrid AC/DC system in North China with different fault locations and resistances to verify the effectiveness of the proposed method. Finally, we further validate the proposed method by leveraging on-site recorded fault data. [ABSTRACT FROM AUTHOR]

The 100 T pulsed magnetic field project is under development at the Wuhan National High Magnetic Field Center, Wuhan, China. Using the NI CompactRIO hardware and Lab-VIEW software platforms, a control and data acquisition system (CADAS) for the 100 T pulsed high magnetic field facility is designed and implemented. This paper briefly introduces the design of the control system and presents the aspects of the implementation. The CADAS developed in this paper communicates with three types of power supply systems and controls their discharge coordinately, and records discharge waveforms. It has been put into test, and 67.6 T pulsed magnetic field has been generated by a test three-coil magnet. [ABSTRACT FROM AUTHOR]

The 2018 RADECS Workshop and 2nd International Conference on Radiation Effects of Electronic Devices (ICREED) were held on May 16–18, 2018, at the Fengda International Hotel, Beijing, China. The workshop and conference were hosted by the China Academy of Aerospace Electronics Technology, organized by the Beijing Microelectronics Technology Institute and the Harbin Institute of Technology, and attended by more than 260 researchers from 15 countries and more than 70 institutions. Six papers presented at ICREED are included in the June 2019 issue of the IEEE Transactions on Nuclear Science. These papers highlight active research on radiation effects on microelectronics in China. Additional papers based on work presented at ICREED appear in other issues of the transactions. [ABSTRACT FROM AUTHOR]

Drones, or general UAVs, equipped with cameras have been fast deployed with a wide range of applications, including agriculture, aerial photography, and surveillance. Consequently, automatic understanding of visual data collected from drones becomes highly demanding, bringing computer vision and drones more and more closely. To promote and track the developments of object detection and tracking algorithms, we have organized three challenge workshops in conjunction with ECCV 2018, ICCV 2019 and ECCV 2020, attracting more than 100 teams around the world. We provide a large-scale drone captured dataset, VisDrone, which includes four tracks, i.e., (1) image object detection, (2) video object detection, (3) single object tracking, and (4) multi-object tracking. In this paper, we first present a thorough review of object detection and tracking datasets and benchmarks, and discuss the challenges of collecting large-scale drone-based object detection and tracking datasets with fully manual annotations. After that, we describe our VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South. Being the largest such dataset ever published, VisDrone enables extensive evaluation and investigation of visual analysis algorithms for the drone platform. We provide a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and conclude the challenge as well as propose future directions. We expect the benchmark largely boost the research and development in video analysis on drone platforms. All the datasets and experimental results can be downloaded from https://github.com/VisDrone/VisDrone-Dataset. [ABSTRACT FROM AUTHOR]

Osteosarcoma is a malignant bone tumor commonly found in adolescents or children, with high incidence and poor prognosis. Magnetic resonance imaging (MRI), which is the more common diagnostic method for osteosarcoma, has a very large number of output images with sparse valid data and may not be easily observed due to brightness and contrast problems, which in turn makes manual diagnosis of osteosarcoma MRI images difficult and increases the rate of misdiagnosis. Current image segmentation models for osteosarcoma mostly focus on convolution, whose segmentation performance is limited due to the neglect of global features. In this paper, we propose an intelligent assisted diagnosis system for osteosarcoma, which can reduce the burden of doctors in diagnosing osteosarcoma from three aspects. First, we construct a classification-image enhancement module consisting of resnet18 and DeepUPE to remove redundant images and improve image clarity, which can facilitate doctors' observation. Then, we experimentally compare the performance of serial, parallel, and hybrid fusion transformer and convolution, and propose a Double U-shaped visual transformer with convolution (DUconViT) for automatic segmentation of osteosarcoma to assist doctors' diagnosis. This experiment utilizes more than 80,000 osteosarcoma MRI images from three hospitals in China. The results show that DUconViT can better segment osteosarcoma with DSC 2.6% and 1.8% higher than Unet and Unet++, respectively. Finally, we propose the pixel point quantification method to calculate the area of osteosarcoma, which provides more reference basis for doctors' diagnosis. [ABSTRACT FROM AUTHOR]

Teaching how to conduct productive research in a classroom setting is a challenge. This article reports on an attempt to scale up the research training in a class of 41 undergraduate and graduate students. The research topic, information aesthetics, covers information visualization and generative art. Seven teams were set up to conduct seven different projects within the topic, and several were able to produce quality research papers published or accepted by journals and conferences. The authors report on their approaches to teaching information aesthetics as a research topic, team set up, and team assessment as well as on the final results in terms of research output. The most important learning experiences in teaching this class were determining how to enable effective team work and maximize the students' research potential. [ABSTRACT FROM PUBLISHER]

In this paper, our objective is to propose a stability concept within the graph model for conflict resolution (GMCR) that does not require knowledge about preferences of other decision makers (DMs) in the conflict and is flexible to analyze the conflict with variable horizon. For that we propose the use of the maximin decision rule within the GMCR. More specifically, we consider a GMCR with two DMs and introduce the concept of Maximinh stability with horizon h for a given DM. The Maximinh stability concept was inspired by the notion of limited-move stability with horizon h present in the GMCR literature and it is adequate for modeling cautious DMs in conflicts where they have no knowledge about the other DM’s preferences. We establish what is the relationship among the Maximinhstabilities for different horizons and the relationship among Maximinh stability and other GMCR stability concepts. Finally, we present an application to illustrate the proposed stability concept, namely, a neuroscience technological selection conflict in China. [ABSTRACT FROM AUTHOR]

The popularity of online contents in social media frequently experiences ebb and flow, and thus its evolution often involves different stages, such as burst and valley. Exploring the patterns of popularity evolution, especially how burst forms and decays, and even further, predicting the trends of popularity evolution is both an important research topic and beneficial to support decision making for many applications, such as emergency management, business intelligence, and public security. Previous work on popularity prediction has focused on predicting the popularity volume of online contents, and at most, popularity burst and ignored the exploration of popularity evolution and the prediction of its stages. To fill this gap, in this paper, we propose our method for the popularity stage prediction problem both at the microscopic level and macroscopic level. At the microscopic level, we first extract multiple dynamic factors and infer future evolution stage by considering the contributions of different dynamic factors. At the macroscopic level, we extract the overall evolution patterns of popularity stages and adopt a pattern matching-based method to predict future popularity stages. We evaluate the proposed approach using tweets in SinaWeibo, the most popular Twitter-like social media platform in China. The experimental results show the effectiveness of our proposed approach in predicting popularity evolution stages. [ABSTRACT FROM AUTHOR]

The balancing of civil airspace capacities with customer demands is achieved through air traffic management (ATM), which uses decision support tools in ATM systems that are ensured by advanced communications and contemporary navigation technologies. However, uncertainties and perturbations can emerge during low-altitude airspace traffic management when considering plans to gradually open up low-altitude airspace in China. Domino effects of time and space deviations to one trajectory may lead to the generation of other 4-D trajectories (4-D trajectories defined based on a series of sequential waypoints recording three dimensions of spatial information and their relevant time information) due to strong levels of spatiotemporal connectivity between trajectories, rendering management challenging. This paper introduces background information and a literature review about conflict detection and resolution (CD&R) for open low-altitude airspace. From our analysis, a CD algorithm for a spatial grid partition system (SGPS) is presented. A conflict resolution (CR) framework of temporally and spatially integrated strategies including the time scheduling-based technique and vertical change-based technique (VCBT) was applied to the CR process. The CD&R algorithm was tested based on a practical maneuver scenario to check its validity, and an analysis of computational performance levels was conducted through several experiments. Airspace capacities can be enhanced to alleviate local airspace network perturbations through the use of the proposed CD&R algorithm. [ABSTRACT FROM AUTHOR]

Due to the geographic features and fruitful water resources, the distributed generation of Small Hydropower Generator Stack (SHGS) are vigorously developed in southwest China. As the more concerns about security and stability of power grids with the large amount of SHGS feed into the grid, the accurate models are critical to analyze those potential impacts. With the development of online monitoring technologies in smart grid, PMU based dynamic equivalent modeling for SHGS drawn many attentions. However, field applications reveal that traditional SHGS equivalent model may not be robustness enough to adapt the different disturbances. To overcome this deficiency, method to improve the robustness of SHGS dynamic equivalent modeling is proposed. In the paper, a coherency identification based multi-machine modeling method is introduced to enhance the accuracy of equivalent model. Sensitivity and correlation analysis based key parameters selection scheme is implemented to select the critical parameters, meanwhile avoiding the multiple solutions problem. Moreover, multi-objective optimization algorithm is designed for key parameters identification of the equivalent model. Additionally, hybrid dynamic simulation has adopted into the equivalent process to improve the computational efficiency. The effectiveness of the proposed method has been validated with an actual case from China Southwest grids. [ABSTRACT FROM AUTHOR]

With the advantage of quick response and flexible ramp, energy storage system (ESS) offers a promising capability of fast frequency control for power systems, especially under a severe disturbance. This paper proposes an ESS control strategy using local measurement in order to provide fast frequency support right after a sufficiently severe disturbance is observed. The proposed strategy includes online frequency characteristic estimator and online optimization controller. Distinguished from existing approaches, no prior knowledge of power system model is required in this work, including generator configuration, operation condition, and frequency response of each component. Instead, the overall system frequency characteristic is approximated online by the proposed estimator. Then, the power setpoint of ESS is determined adaptively to participate in frequency control by the proposed model predictive controller. Finally, the effectiveness of the proposed method is verified via real-world data of a provincial power grid in China. [ABSTRACT FROM AUTHOR]

This paper develops a hierarchical deep learning machine (HDLM) to efficiently achieve both quantitative and qualitative online transient stability prediction (TSP). For the sake of improving its online efficiency, multiple generators’ fault-on trajectories as well as the two closest data-points in pre-/post-fault stages are acquired by PMUs to form its raw inputs. An anti-noise graphical transient characterization technique is tactfully designed to transform multiplex trajectories into 2-D images, within which system-wide transients are concisely described. Then, following the divide-and-conquer philosophy, the HDLM trains a two-level convolutional neural network (CNN) based regression model. With stability margin regressions hierarchically refined, it manages to perform reliable and adaptive online TSP almost immediately after fault clearance. Test results on the IEEE 39-bus test system and the real-world Guangdong Power Grid in South China demonstrate the HDLM's superior performances on both stability status and stability margin predictions. [ABSTRACT FROM AUTHOR]

Accurate and reliable low-voltage load forecasting is critical to optimal operation and control of distribution network and smart grid. However, compared to traditional regional load forecasting at high-voltage level, it faces tough challenges due to the inherent high uncertainty of the low-capacity load and distributed renewable energy integrated in the demand side. This paper proposes a novel hybrid ensemble deep learning (HEDL) approach for deterministic and probabilistic low-voltage load forecasting. The deep belief network (DBN) is applied to low-voltage load point prediction with the strong ability of approximating nonlinear mapping. A series of ensemble learning methods including bagging and boosting variants are introduced to improve the regression ability of DBN. In addition, the differencing transformation technique is utilized to ensure the stationarity of load time series for the application bagging and boosting methods. On the basis of the integrated thought of ensemble learning, a new hybrid ensemble algorithm is developed via integrating multiple separate ensemble methods. Considering the diversity in various ensemble algorithms, an effective K nearest neighbor classification method is utilized to adaptively determine the weights of sub-models. Furthermore, HEDL based probabilistic forecasting is proposed by taking advantage of the inherent resample idea in bagging and boosting. The effectiveness of the HEDL method for both deterministic and probabilistic forecasting has been systematically verified based on realistic load data from East China and Australia, indicating its promising prospective for practical applications in distribution networks. [ABSTRACT FROM AUTHOR]