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We are pleased to announce that the June 2022 issue of the IEEE Transactions on Dielectrics and Electrical Insulation (TDEI) will be a special issue on electrets and related phenomena. This issue is open to all authors. Presenters of papers at the IEEE 18th International Symposium on Electrets (ISE18) to be held during September 24–28, 2021 in Shanghai (China) are especially encouraged to submit their work to this special issue. For more information on ISE18 please go to https://ise18.tongji.edu.cn/. [ABSTRACT FROM AUTHOR]

Understanding intra-urban human mobility patterns and their potential driving forces are vital to city planning and commercial site selection. In this paper, we first investigate the functions of urban regions and how different region types dynamically influence people’s trip decisions. Furthermore, we characterize urban circadian rhythms by time-vary inter-regional transition probabilities between these regions with different functions, and integrate them into intervening opportunity model to predict human mobility. Public transportation card data in Shanghai are used to demonstrate the effectiveness of the model in terms of station passenger flows, travel time and trip flux. By taking regional function into consideration, the proposed model significantly improved the prediction accuracy. Quantitative analysis ulteriorly indicates that trip intentions and regional features are critical elements in trip flux prediction, especially in the afternoon and evening when people have an abundance of opportunities to travel by their own volition. When the function of a certain region changes, our model is able to make reasonable predictions accordingly. The results indicate the importance of considering individual travel motivation and regional function in modeling human mobility. The proposed model could serve as a guide for popularity and trip flux prediction in urban planning and reconstruction. [ABSTRACT FROM AUTHOR]

With widespread deployment of GPS devices, massive spatiotemporal trajectories became more accessible. This booming trend paved the solid data ground for researchers to discover the regularities or patterns of human mobility. However, there are still three challenges in semantic pattern extraction including semantic absence, semantic bias and semantic complexity. In this paper, we invent and apply a novel data structure namely City Semantic Diagram to overcome above three challenges. First, our approach resolves semantic absence by exactly identifying semantic behaviours from raw trajectories. Second, the design of semantic purification helps us to detect semantic complexity from human mobility. Third, we avoid semantic bias using objective data source such as ubiquitous GPS trajectories. Comprehensive and massive experiments have been conducted based on real taxi trajectories and points of interest in Shanghai. Compared with existing approaches, City Semantic Diagram is able to discover fine-grained semantic patterns effectively and accurately. [ABSTRACT FROM AUTHOR]

Inevitable irregularities excite a traveling maglev train along a journey by disturbing air gaps and thus modifying levitation forces. For a half bogie which is a rigid coupled structure with a separate levitation unit at each endpoint, the effects of track irregularities hamper the synchronization of the two units. Motivated by the aforementioned, this paper establishes a newly nonlinear model for the half bogie considering irregularities, internal and external disturbances. Based on this model, a prescribed-time synchronization controller (PTSC) considering input constraints is designed while two adaptive disturbance observers (ADOs) are combined to address track irregularities and disturbances. To reduce the actuating times caused by synchronization, a lazy cooperation mode is adopted by independently introducing an event-triggered mechanism for each levitation unit. Theoretical analysis establishes the stability of the whole control scheme and demonstrates that the tracking errors and estimate errors can be arbitrarily small within a prescribed time, which can be determined by users through a parameter, and the synchronization is achieved. Numerical simulations compared with other two control schemes verify the effectiveness of the proposed control scheme, where both the ideal irregularity model and the field data measured from the Shanghai commercial line are tested. [ABSTRACT FROM AUTHOR]

High-precision road detection from very high resolution (VHR) remote sensing images has broad application value. However, the most advanced deep learning based methods often fail to identify roads when there is a distribution discrepancy between the training samples and test samples, due to their limited generalization ability. In this paper, to address this problem, an open-source data-driven domain-specific representation (OSM-DOER) framework is proposed for cross-domain road detection. On the one hand, as the spatial structure information of the source and target domains is similar, but the texture information is different, the domain-specific representation (DOER) framework is proposed, which not only aligns the distributions of the spatial structure information, but also learns the domain-specific texture information. Furthermore, in order to enhance the representation of the target domain data distribution, open-source and freely available OpenStreetMap (OSM) road centerline data are utilized to generate target domain samples, which are then used in the network training as the supervised information for the target domain. Finally, to verify the superiority of the proposed OSM-DOER framework, we conducted extensive experiments with the public SpaceNet and DeepGlobe road datasets, and large-scale road datasets from Birmingham in the UK and Shanghai in China. The experimental results demonstrate that the proposed OSM-DOER framework shows obvious advantages over the mainstream road detection methods, and the use of OSM road centerline data has great potential for the road detection task. [ABSTRACT FROM AUTHOR]

In structural health monitoring (SHM), measuring and evaluating structural dynamic responses are critical for safety management of civil infrastructures. Particularly, online forecasting of the structural responses under extreme external loading conditions (e.g., earthquakes) takes a significant role in SHM to provide early warning and ensure safe operation. In practice, complex causality and intrinsic interactions between seismic excitation and structural response make it challenging to establish a reliable predictive scheme. The present paper proposes a novel deep recurrent neural network (RNN) model implemented in the architecture of a time-series attention-based RNN encoder–decoder (TSA-RNN-ED), for predictive analysis of structural responses under seismic excitation. In the proposed data-driven model, upcoming sequential responses are predicted through sequence-to-sequence learning from historical multivariate time-series signals. A time-series attention mechanism is proposed to exploit the heterogeneous, but directly related, hidden features between the seismic loads and the corresponding structural responses. The proposed architecture can reliably regress excitation-response interactions to predict dynamic responses subjected to future earthquakes while satisfying the need of real-time forecasting for on-site practical implementation. This article systematically evaluates the proposed model by using two real-world structural cases: 1) the tallest building in China, the Shanghai Tower and 2) a woodframe classroom on a shake table at the University of British Columbia in Vancouver, Canada. The experimental results demonstrate the accurate and efficient performance of the proposed methodology in forecasting the seismic responses of the structures under investigation. [ABSTRACT FROM AUTHOR]

Nowadays, the autonomous driving transportation systems are at the heart of both academic and industry research for the distinguished advantages including increased network capacity, enhanced punctuality, greater flexibility and improved overall safety level. With the responsibility of transporting passengers in a safe, comfortable and efficient way, the decision making method plays a critical position in the autonomous driving of high-speed trains. Focusing on solving the autonomous decision making problem, this paper proposes a novel learning based framework by combining the deep learning technology with the distributed tracking control approach. To cope with the data insufficiency problem in training the deep learning network, a generative adversarial network (GAN) based data argumentation scheme is proposed to generate data samples that have the same distribution with actual data samples, and a hybrid learning network is constructed to predict the speed trajectory from the multi-attribute data with both temporal sequences and static features. Then, based on the model predictive control (MPC) scheme, a distributed tracking control model is formulated to minimize the tracking deviations and balance the performance of punctuality, energy-efficiency and riding comfort. Further, the dual decomposition technique is adopted to deal with the coupling constraints for the safe distance headway such that the separation for the autonomous driving of high-speed trains is achieved. Finally, simulation experiments based on actual scenarios of the Beijing-Shanghai high-speed railway are conducted to illustrate the effectiveness of our methods. [ABSTRACT FROM AUTHOR]

Virtual Coupling (VC) is regarded as a breakthrough to the traditional train operation and control for improving the capability and flexibility in railways. It brings benefits as trains under VC are allowed to operate much closer to one another, forming a virtually coupled train set (VCTS). However, the safe and stable spacing between trains in the VCTS is a problem since there are no rigid couplers to connect them into a fixed formation, especially in high-speed scenarios. Due to the close spacing, the interference between trains becomes non-negligible as various maneuvers of the preceding train can significantly affect driving behaviors of the following train; this results in fluctuating spacing and therefore an unstable VCTS. Aiming at minimizing the interference and maintaining constantly safe spacing between trains in the VCTS, this paper presents a distributed model predictive control (DMPC) approach for solving the high-speed VCTS control problem. Particularly, the proposed control method focuses on the feasibility and stability of this problem, with considerations of the coupled constraint of safety braking distance and the individual constraints of speed limit variations and restricted traction/braking performance. To guarantee feasibility and stability, the terminal controller and invariant set of the DMPC are designed. For rigor, sufficient conditions of feasibility and stability are mathematically proved and derived. Based on the data of the Beijing-Shanghai high-speed railway line, numerical experiments are conducted to verify the correctness of derived sufficient conditions and the effectiveness of the proposed control method under interference and disturbances. [ABSTRACT FROM AUTHOR]

Currently, self-driving, emerging as a key automatic application, has brought a huge potential for the provision of in-vehicle services (e.g., automatic path planning) to mitigate urban traffic congestion and enhance travel safety. To provide high-quality vehicular services with stringent delay constraints, edge computing (EC) enables resource-hungry self-driving vehicles (SDVs) to offload computation-intensive tasks to the edge servers (ESs). In addition, caching highly reusable contents decreases the redundant transmission time and improves the quality of services (QoS) of SDVs, which is envisioned as a supplement to the computation offloading. However, the high mobility and time-varying requests of SDVs make it challenging to provide reliable offloading decisions while guaranteeing the resource utilization of content caching. To this end, in this paper we propose a collaborative computation offloading and content caching method, named CoPace, by leveraging deep reinforcement learning (DRL) in EC for self-driving system. Specifically, we first introduce OSTP to predict the future time-varying content popularity, taking into account the temporal-spatial attributes of requests. Moreover, a DRL-based algorithm is developed to jointly optimize the offloading and caching decisions, as well as the resource allocation (i.e., computing and communication resources) strategies. Extensive experiments with real-world datasets in Shanghai, China, are conducted to evaluate the performance, which demonstrates that CoPace is both effective and well-performed. [ABSTRACT FROM AUTHOR]

Fast insecurity early warning is the key technique to resist the dynamic insecurity risk, which becomes intractable due to the strong nonlinearity of hybrid AC/DC grids and the high uncertainty of wind generation. Considering dynamic security constraints and wind power uncertainty, this paper presents an insecurity early warning method based on decision tree (DT) and semi-supervised deep learning. First, semi-supervised deep learning is deployed to estimate the dynamic security limit of the critical interface of hybrid AC/DC grids. The system security is assessed by comparing the actual power transfer of the critical interface with the security limit. Then, operating conditions (OCs) are ranked into different insecure levels according to the type of preventive control actions that is needed to ensure the system security. Finally, oblique DT is utilized to identify insecurity classification boundaries in the wind power injection space. Insecure OC sets are constructed based on these classification boundaries. Simulation results of the real-life Jiangsu-Shanghai interconnected grid in east China demonstrate that the proposed method can fast construct the insecure OC sets corresponding to different insecure levels. [ABSTRACT FROM AUTHOR]

The superconducting undulator (SCU) is a trend to be applied for future light sources to obtain the higher light brilliance. A planar SCU prototype with a period of 16 mm, a magnetic gap of 9.5 mm and 50-periods magnet has been under development at the Shanghai Synchrotron Radiation Facility (SSRF) since late 2013. The key technologies including magnet cooling, coil winding, low-temperature alignment of magnet and beam tube, and cryogenic magnetic field measurement have been investigated. A cryocooler-cooled test cryostat was developed for the purpose of both experimentally studying the key technologies and testing the performance of the SCU prototype. Two-stage cryocoolers were applied for cool down, helium liquefaction and keeping the SCU cooled at operation. The SCU magnet and the UHV beam tube were cooled separately. The magnet working at 4.2 K is cooled by thermosiphon-driven liquid helium flowing through the piping in the magnet mandrel. The beam tube, thermal shields and binary leads were conduction-cooled. The beam tube will work at between 4.2 K and 20 K up to dynamic loads generated by the SSRF beam. A set of improved self-centered support assembly for the magnet was developed in order to avoid the complex alignment at low temperature and lower down the machining cost. A cooling approach to effectively cool the junction of the binary lead made of copper and HTS lead commonly used in cryocooler-cooled superconducting systems was adopted and validated. The cryostat was successfully debugged and run at the early of 2016. This paper describes the design details of the cryostat including new approaches to cool the SCU and the binary leads, its fabrication and test results. [ABSTRACT FROM AUTHOR]

Global navigation satellite system multipath channel modeling is important for signal simulation and error mitigation in urban scenarios. However, there have not been sufficient studies on channel statistical models for dynamic land vehicles in eastern Asian cities. This paper presents both MULTIPATH and non-line-of-sight (NLOS) channel models for GPS L1CA and BDS B1I signals. They are based on an extensive field dataset collected in the Shanghai-Lujiazui area. In addition, a method is developed to estimate multipath components and NLOS signal parameters in a dynamic channel environment. It is found that the Gamma function best fits the distribution of MULTIPATH channel delays while the exponential function is better suited for the distribution of NLOS channel delays. Both MULTIPATH and NLOS channel signals follow a linear decline average power-delay trend and a zero-mean Gaussian distribution power scattering model. The Doppler fading frequency distribution for MULTIPATH and NLOS channels can be modeled by the Gaussian function except for the MULTIPATH channel signal at the low velocity range (0∼3 km/h), where an absolute exponential distribution is a better fit. The lifetime distribution for MULTIPATH and NLOS channels is represented by the generalized Pareto distribution function. These findings offer insights into the GNSS signal models in urban propagation channels and are essential to the development of accurate urban navigation systems. [ABSTRACT FROM AUTHOR]

Massive data broadcast by GPS-equipped vehicles provide unprecedented opportunities. One of the main tasks in order to optimize our transportation networks is to build data-driven real-time decision support systems. However, the dynamic environments where the networks operate disallow the traditional assumptions required to put in practice many off-the-shelf supervised learning algorithms, such as finite training sets or stationary distributions. In this paper, we propose BRIGHT: a drift-aware supervised learning framework to predict demand quantities. BRIGHT aims to provide accurate predictions for short-term horizons through a creative ensemble of time series analysis methods that handles distinct types of concept drift. By selecting neighborhoods dynamically, BRIGHT reduces the likelihood of overfitting. By ensuring diversity among the base learners, BRIGHT ensures a high reduction of variance while keeping bias stable. Experiments were conducted using three large-scale heterogeneous real-world transportation networks in Porto (Portugal), Shanghai (China), and Stockholm (Sweden), as well as with controlled experiments using synthetic data where multiple distinct drifts were artificially induced. The obtained results illustrate the advantages of BRIGHT in relation to state-of-the-art methods for this task. [ABSTRACT FROM AUTHOR]

The magnetic resonance sounding (MRS) method can noninvasively, directly, and quantitatively detect groundwater and forecast the potential for a water-induced disaster during tunnel construction. However, due to the spatial limitations of underground engineering, the signal-to-noise ratio (SNR) of the receiving signal is low (<1), which severely limits the reliability of the disaster warning. Thus, the question of how to design the meter-scale antenna and improve the SNR has become the bottleneck that restricts the successful application of the underground MRS method. To address this problem, we comprehensively consider the factors of detection depth, signal amplitude, and antenna size and weight using theoretical calculations and model simulations in this paper. We determine that an 8-turn transmitting antenna and a 30-turn receiving antenna with both side lengths of 6 m constitute an effective combination for achieving reliable detection of large water-bearing structures. Meanwhile, we propose a front-end matching circuit for a high-inductance antenna to solve the problem of attenuation of the signal amplitude due to high impedance and successfully increase the SNR from 1.12 to 5.13. Finally, in the tunnel of the Shanghai–Kunming high-speed railway of China, we perform the field measurement of an underground river and validate the practicability of the proposed antenna and matching circuit. [ABSTRACT FROM AUTHOR]

In this paper, we propose a game-based policy for the signal control of an isolated intersection together with the rerouting of the vehicles in this intersection using V2I technology. The game starts at the beginning of each signal cycle on the condition that there are vehicles delayed in the intersection at the end of current cycle. Then the delayed vehicles negotiate with the signal controller to decide the signal setting for next cycle while rerouting their next movements. During the game, each delayed vehicle selects one route from their own acceptable route space to reduce the intersection delay time, while the signal controller adjusts the signal setting to meet the traffic demand of the delayed vehicles as well as maximize the throughput. The game can be terminated with end conditions in finite rounds. The vehicles coming after the end of game reroute in a similar way with the delayed vehicles but based on the game results. Numerical experiments on a calibrated network of Caohejing District in Shanghai indicate that our proposed method can effectively mitigate road congestions and improve the network capacity, especially at high traffic loads. [ABSTRACT FROM AUTHOR]

After entering the big data era, a new term of 'big knowledge' has been coined to deal with challenges in mining a mass of knowledge from big data. While researchers used to explore the basic characteristics of big data, we have not seen any studies on the general and essential properties of big knowledge. To fill this gap, this paper studies the concepts of big knowledge, big-knowledge system, and big-knowledge engineering. Ten massiveness characteristics for big knowledge and big-knowledge systems, including massive concepts, connectedness, clean data resources, cases, confidence, capabilities, cumulativeness, concerns, consistency, and completeness, are defined and explored. Based on these characteristics, a comprehensive investigation is conducted on some large-scale knowledge engineering projects, including the Fifth Comprehensive Traffic Survey in Shanghai, the China's Xia-Shang-Zhou Chronology Project, the Troy and Trojan War Project, and the International Human Genome Project, as well as the online free encyclopedia Wikipedia. We also investigate the recent research efforts on knowledge graphs, where they are analyzed to determine which ones can be considered as big knowledge and big-knowledge systems. Further, a definition of big-knowledge engineering and its life cycle paradigm is presented. All of these projects are accordingly checked to determine whether they belong to big-knowledge engineering projects. Finally, the perspectives of big knowledge research are discussed. [ABSTRACT FROM AUTHOR]

In this paper, we present the characteristics of a vehicular ad hoc network (VANET), which is the Shanghai urban vehicular network (SUVnet). We construct a mobility model using the GPS data collected from more than 4000 taxis in Shanghai. The model is both realistic and large scale. Based on this model, network topology and connectivity of SUVnet are studied. Because of the sparse distribution and dynamic topology of SUVnet, simply utilizing the conventional mobile ad hoc network routing protocols in SUVnet may not achieve a satisfactory performance. Therefore, we apply the delay-tolerant network model to SUVnet and evaluate the epidemic routing protocols. We propose a new protocol, which is the distance aware epidemic routing (DAER), to improve the bundle delivery ratio. Results show that DAER performs well for a VANET. This paper provides a basis in studying a realistic urban VANET. [ABSTRACT FROM AUTHOR]

A differentially fed dual-polarized multidipole antenna is proposed in this paper. Two pairs of long dipoles and two pairs of short dipoles are printed on an oriental crown-shaped substrate. Through a rational design of positions of dipoles, stable radiation patterns are achieved at the horizontal plane within a relative bandwidth of 45%. Because of the symmetry of the antenna, its differential port-to-port isolation is theoretically infinite. The antenna is designed, fabricated, and measured. The measurement results are in good agreement with the simulated ones in terms of gain, radiation patterns, and voltage standing wave ratio. Wideband impedance matching and stable radiation patterns are achieved by the proposed antenna. In particular, the half power beamwidth (HPBW) at the horizontal plane maintains a mean value 65° with only 4° variations across the desired band, which is suitable for the requirement of a three-sector base-station antenna. Interestingly, the proposed antenna looks like the famous building: the Oriental Crown-China Pavilion in the World Expo held in Shanghai. [ABSTRACT FROM PUBLISHER]

In this paper, a single frequency network (SFN) application of the digital terrestrial multimedia broadcasting (DTMB) system in Shanghai is presented. A three-stage implementation practice is described in details. Computer simulations of DTMB receiver in severe dynamic multipath channels are presented to verify the method of SFN adjustments. [ABSTRACT FROM PUBLISHER]

In view of the uncertainty and intermittency of wind power, this paper proposes an optimal economical dispatch (ED) model and develops a method to estimate risk and manage hybrid power systems (traditional + wind~power~systems) for the short-term (24 h) operations. The model and the method have taken into account the large wind power penetration and the wind variability. The particle swarm optimization (PSO) refid="ref1"/ algorithm with constraints is applied to solve the ED problem. Value at risk (VaR) refid="ref2"/ and integrated risk management (IRM) refid="ref3"/ are used separately to assess the risk, so that an optimal tradeoff between the profit and risk is made for the system operations. The model and the method are tested on the standard IEEE 30-bus power system and network in Shanghai. The validity of the model and the method has been approved. [ABSTRACT FROM PUBLISHER]

The Shanghai Synchrotron Radiation Facility (SSRF) is a third-generation light source with major emphasis on insertion device (ID) sources. In the storage ring there are 20 straight sections, each about six or ten meters in length, for possible insertion devices. Currently there installed three undulators and two wiggler installed in five straight sections [1]-[3]. They were originally designed and fabricated by the Shanghai Institute of Applied Physics (SINAP). A variable polarizing undulator was constructed as the light source of the soft X-rays beamline for SSRF. This local control system consists of a Siemens PLC and a six axis Masterdrive motion controller. Four servo motors control the gap--two on the upper girder and two on the lower girder, and another two servos controls the phase--one on the upper girder outer and one on the lower girder inner. The main control system at the SSRF is experimental physics and industrial control system (EPICS). This paper will discuss in detail the design philosophy and the implementation of the Six-motor insertion device control system. This control system has been in operation for about one year. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems (ITSs) have become increasingly important for public transportation in Shanghai, China. In response, ShanghaiGrid (SG) aims to provide abundant intelligent transportation services to improve traffic conditions. A fundamental service in SG is to locate the nearest desirable vehicles for users. In this paper, we propose an innovative protocol called ANTS to locate a desirable vehicle close to the querying user. The protocol finely mimics the efficient searching strategy adopted by a lost ant searching for its nest. Taking query locality into account, ANTS can retrieve the closest vehicles satisfying the query with high probability but incurs small query latency and modest network traffic. ANTS is a fully distributed and robust protocol and, therefore, has good scalability. Extensive simulations based on the real road network and the trace data of vehicle movements in Shanghai demonstrate the efficacy of ANTS. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems have become increasingly important for the public transportation in Shanghai. In response, ShanghaiGrid (SG) project aims to provide abundant intelligent transportation services to improve the traffic condition. A challenging service in SG is to accurately locate the positions of moving vehicles in real time. In this paper, we present an innovative scheme, Hierarchical Exponential Region Organization (HERO), to tackle this problem. In SG, the location information of individual vehicles is actively logged in local nodes which are distributed throughout the city. For each vehicle, HERO dynamically maintains an advantageous hierarchy on the overlay network of local nodes to conservatively update the location information only in nearby nodes. By bounding the maximum number of hops the query is routed, HERO guarantees to meet the real-time constraint associated with each vehicle. A small-scale prototype system implementation and extensive simulations based on the real road network and trace data of vehicle movements from Shanghai demonstrate the efficacy of HERO. [ABSTRACT FROM AUTHOR]

Vehicle-based sensors can be used for traffic monitoring. These sensors are usually set with long sampling intervals to save communication costs and to avoid network congestion. In this paper, we are interested in understanding the traffic-monitoring performance that we can expect from such vehicle-based mobile sensor networks, despite the incomplete information provided. This is a fundamental problem to be addressed. A performance evaluation has been carried out in Shanghai, China, by utilizing the vehicle-based sensors installed in about 4000 taxies. Two types of traffic status-estimation algorithms, i.e., the link-based and the vehicle-based, are introduced and analyzed. The results show that estimations of the traffic status based on these imperfect data are reasonably accurate. Therefore, the feasibility of such an application is demonstrated. [ABSTRACT FROM AUTHOR]

Describes the above-named upcoming special issue or section. May include topics to be covered or calls for papers. [ABSTRACT FROM PUBLISHER]