North China University of Technology

Accurate and timely traffic flow information is important for the successful deployment of intelligent transportation systems. Over the last few years, traffic data have been exploding, and we have truly entered the era of big data for transportation. Existing traffic flow prediction methods mainly use shallow traffic prediction models and are still unsatisfying for many real-world applications. This situation inspires us to rethink the traffic flow prediction problem based on deep architecture models with big traffic data. In this paper, a novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently. A stacked autoencoder model is used to learn generic traffic flow features, and it is trained in a greedy layerwise fashion. To the best of our knowledge, this is the first time that a deep architecture model is applied using autoencoders as building blocks to represent traffic flow features for prediction. Moreover, experiments demonstrate that the proposed method for traffic flow prediction has superior performance.

Conventional model predictive current control (MPCC) is a powerful control strategy for three-phase inverters that has the advantages of simple concept, quick response, easy implementation, and good performance. However, MPCC is sensitive to machine parameter variation, and the performance degrades substantially if a mismatch exists between the model parameters and real machine parameters. Model-free predictive current control (MFPCC) based on an ultralocal model, which uses only the input and output of the system without considering any motor parameters, has been proposed to solve this problem in this article. Since parameters are not required, the robustness of the control system is improved. However, conventional MFPCC based on an ultralocal model uses many control parameters, which increases the tuning work. Furthermore, the control performance is not ideal at low sampling frequency. This article proposes an improved MFPCC based on the extended state observer of PMSM drives that does not require motor parameters and needs less tuning work and lower computational time while achieving the better performance in terms of current harmonics, tracking error, and dynamic overshoot. The proposed method is compared to conventional MPCC and MFPCC, and the effectiveness is confirmed by the simulation and experimental results.

In order to solve the parameter dependence problem in model predictive control, an improved model predictive current control (MPCC) method based on the incremental model for surface-mounted permanent-magnet synchronous motor drives is proposed in this paper. First, the parameter sensitivity of a conventional MPCC method is analyzed, which indicates that the parameter mismatches would cause prediction current error and inaccurate delay compensation. Therefore, an incremental prediction model is introduced in this paper to eliminate the use of permanent magnetic flux linkage in a prediction model. Among the parameter of the incremental prediction model, only inductance mismatch contributes to the prediction error, since the influence of resistance mismatch on the control performance is very small. Therefore, in order to improve the antiparameter-disturbance capability of the MPCC method, an inductance disturbance controller, which includes the inductance disturbance observer and inductance extraction algorithm, is presented to update accurate inductance information for the whole control system in real time. Finally, simulation and experimental results both show that the proposed method can effectively eliminate the influence of the parameter mismatches on the control performance and reduce the parameter sensitivity of the MPCC method.

Markus and Kitayama's (1991) theory of independent and interdependent self-construals had a major influence on social, personality, and developmental psychology by highlighting the role of culture in psychological processes. However, research has relied excessively on contrasts between North American and East Asian samples, and commonly used self-report measures of independence and interdependence frequently fail to show predicted cultural differences. We revisited the conceptualization and measurement of independent and interdependent self-construals in 2 large-scale multinational surveys, using improved methods for cross-cultural research. We developed (Study 1: N = 2924 students in 16 nations) and validated across cultures (Study 2: N = 7279 adults from 55 cultural groups in 33 nations) a new 7-dimensional model of self-reported ways of being independent or interdependent. Patterns of global variation support some of Markus and Kitayama's predictions, but a simple contrast between independence and interdependence does not adequately capture the diverse models of selfhood that prevail in different world regions. Cultural groups emphasize different ways of being both independent and interdependent, depending on individualism-collectivism, national socioeconomic development, and religious heritage. Our 7-dimensional model will allow future researchers to test more accurately the implications of cultural models of selfhood for psychological processes in diverse ecocultural contexts. (PsycINFO Database Record

In order to optimize the current-control performance of the permanent-magnet synchronous motor (PMSM) system with model parameter mismatch and one-step control delay, an improved deadbeat predictive current control (DPCC) algorithm for the PMSM drive systems is proposed in this paper. First, the performance of the conventional predictive current control, when parameter mismatch exist, is analyzed, and then a stator current and disturbance observer (SCDO) based on sliding-mode exponential reaching law, which is able to simultaneously predict future value of stator current and track system disturbance caused by parameter mismatch in real time, is proposed. Based on this SCDO, prediction currents are used for replacing the sampled current in DPCC to compensate one-step delay, and estimated parameter disturbances are considered as the feedforward value to compensate the voltage reference calculated by deadbeat predictive current controller. Thus, a composite control method combining the DPCC part and current prediction and feedforward compensation part based on SCDO, called DPCC + SCDO method, is developed. Moreover, based on conventional exponential reaching law, a novel sliding-mode exponential reaching law is proposed to further improve the performance of the DPCC + SCDO method. Simulation and experimental results both show the validity of the proposed current control approach.

With the development of Internet-of-Things (IoT) technology, various types of information, such as social resources and physical resources, are deeply integrated for different comprehensive applications. Social networking, car networking, medical services, video surveillance, and other forms of the IoT information service model gradually change people's daily lives. Facing the vast amounts of IoT information data, the IoT search technology is used to quickly find accurate information to meet the real-time search needs of users. However, IoT search requires using a large amount of user private information, such as personal health information, location information, and social relations information, to provide personalized services. Employing private information from users will encounter security problems if an effective access control mechanism is missing during the IoT search process. An access control mechanism can effectively monitor the access activities of resources and ensure that authorized users access information resources under legitimate conditions. This survey examines the growing literature on access control for an IoT search. Problems and challenges of access control mechanisms are analyzed to facilitate the adoption of access control solutions in real-life settings. This article aims to provide theoretical, methodological, and technical guidance for IoT search access control mechanisms in large-scale dynamic heterogeneous environments. Based on a literature review, we also analyzed the future development direction of access control in the age of IoT.

Conventional model predictive torque control (MPTC) suffers from weighing factor tuning work and relatively high torque ripple, due to the different units of torque and stator flux and the limited number of voltage vectors. This paper proposes an improved MPTC without the use of weighting factor. The torque and flux magnitude references are converted into an equivalent reference vector of stator flux, hence eliminating the weighting factor of stator flux in conventional MPTC. Furthermore, two voltage vectors are applied during one control period to achieve better steady-state performance. Different from prior method using an active vector and a zero vector, the selected voltage vectors may be two nonzero vectors in the proposed method, which provides more opportunities to reduce both torque and flux ripples. The durations of the selected voltage vectors are determined based on the principle of stator flux error minimization. Both simulation and experimental results are presented to validate the effectiveness of the proposed method.

In this article, in order to optimize the dynamic performance of the permanent magnet synchronous motor (PMSM) speed regulation system, a nonlinear speed-control algorithm for the PMSM control systems using sliding-mode control is developed. First, a sliding-mode control method based on a new sliding-mode reaching law (NSMRL) is proposed. This NSMRL includes the system state variable and the power term of sliding surface function. In particular, the power term is bounded by the absolute value of the switching function, so that the reaching law can be expressed in two different forms during the reaching process. This method can not only effectively suppress the inherent chattering, but also increases the velocity of the system state reaching to the sliding-mode surface. Based on this new reaching law, a sliding-mode speed controller (SMSC) of PMSM is designed. Then, considering the large chattering phenomenon caused by high switching gain, an improved antidisturbance sliding-mode speed controller method, called SMSC + ESO method, is developed. This method introduces an extended state observer to observe the lumped disturbance and adds a feedforward compensation item based on the observed disturbances to the SMSC. Finally, simulation and experimental results both show the validity of the proposed control method.

Bismuth ferrite-based lead-free multilayers with a high recoverable energy density of 6.74 J cm ⁻³ and an efficiency of 77%, stable up to 125 °C.

Model predictive torque control (MPTC) is emerging as a powerful control scheme for high performance control of induction motor (IM) drives. Compared to direct torque control, MPTC is more effective and accurate in voltage vector selection by incorporating the system model directly with the finite switching states. However, for two-level inverter-fed IM drives, the sampling frequency of MPTC has to be high to achieve good performance due to the limited number of voltage vectors. Recently, the concept of duty cycle control was introduced in MPTC by inserting a null vector along with the selected active voltage vector to achieve torque ripple reduction. The active vector is first selected from conventional MPTC and then its duration is determined based on a certain principle. The cascaded processing of active vector and its duration leads to poor low-speed performance and the sampling frequency still has to be high. This paper proposes an improved MPTC with duty cycle control by optimizing the vector selection and its duration simultaneously when minimizing both torque and flux errors. As a result, better steady-state performance at both low and high speeds is achieved, even if the sampling frequency is reduced by half. The effectiveness of the proposed MPTC is verified by both simulation and experimental results.

Entrepreneurship is one of the important engines of economic development. Under the influence of policy encouragement and economic situation, college students have become the emerging entrepreneurial subjects. Studying the factors influencing their willingness to innovate is conducive to improving the entrepreneurial status and performance. From the perspective of planned behavior theory, this paper analyzes the effects of college students' entrepreneurship education and self-efficacy on their entrepreneurial intention. Using a sample of 327 college students in China, we test the hypotheses, and get some results. Firstly, college students' entrepreneurial education has a significant positive effect on their entrepreneurial intention, but has no obvious effect on the entrepreneurial attitude. Secondly, college students' entrepreneurial self-efficacy has a significant positive effect on the entrepreneurial attitude and entrepreneurial intention, and the entrepreneurial attitude plays a partial intermediary role in the relationship between entrepreneurial self-efficacy and entrepreneurial intention.

This note studies the controllability of a leader-follower network of dynamic agents linked via neighbor rules. The leader is a particular agent acting as an external input to steer the other member agents. Based on switched control system theory, we derive a simple controllability condition for the network with switching topology, which indicates that the controllability of the whole network does not need to rely on that of the network for every specific topology. This merit provides convenience and flexibility in design and application of multiagent networks. For the fixed topology case, we show that the network is uncontrollable whenever the leader has an unbiased action on every member, regardless of the connectivity of the members themselves. This gives new insight into the relation between the controllability and the connectivity of the leader-follower network. We also give a formula for formation control of the network.

In order to reduce the computation burden and eliminate the weighting factor in conventional model predictive torque control (MPTC), this paper proposes an improved MPTC algorithm without the use of weighting factor. Based on the deadbeat direct torque and flux control, the reference voltage vector, which would be applied in the next period, is predicted to avoid testing all feasible voltage vectors, hence reducing the computation burden of conventional MPTC. Moreover, the torque and flux error-based cost function of conventional MPTC is replaced by the voltage vector tracking error-based cost function; thus, the weighting factor of stator flux in conventional MPTC, which is always necessary because of the different units between torque and flux, is eliminated. Furthermore, two voltage vectors, which include an active vector and a null vector, are applied during one control period to improve steady-state performance. The durations of the selected voltage vectors are determined based on the principle of voltage vector tracking error minimization. Moreover, to further improve the steady-state performance, a different vector selection way, in which the second voltage vector is not fixed as a null vector but selected in a border range, is introduced. The selection process of candidate voltage vectors is analyzed, and the duration is calculated. Simulation and experimental results both show the validity of the proposed control approach.

This paper proposes an improved model predictive direct power control (MPDPC) for a pulse width modulation (PWM) rectifier by using a duty cycle control. The conventional MPDPC achieves good steady-state performance and quick dynamic response by selecting the best voltage vector, which minimizes the errors between the reference power and the real power. However, due to the limited number of voltage vectors in a two-level converter, the sampling frequency has to be high to achieve satisfactory performance. This paper introduces the concept of a duty cycle control in the MPDPC by allocating a fraction of control period for a nonzero voltage vector and the rest time for a zero vector. The nonzero vector is selected by evaluating the effects of each nonzero vector and its duration is obtained based on the principle of power errors minimization. Simulation and experimental results prove that, compared to the conventional MPDPC, the proposed MPDPC with duty cycle achieves further steady-state performance improvement without affecting the dynamic response at a small cost of control complexity increase.

Model-predictive current control (MPCC) is widely recognized as a high-performance control strategy of permanent magnet synchronous machine (PMSM) drives due to its quick response and simple principle. It uses a cost function to select the best voltage vector minimizing the current error between the reference value and the feedback value. However, as only one voltage vector is applied during one control period, it fails to give satisfactory performance due to the limited voltage vectors, especially in the case of two-level converters. This paper proposes an improved MPCC strategy for PMSM drives, which first estimates the back electromotive force (EMF) based on the past value of stator voltage and currents and then applies the estimated EMF in the stator current prediction. To achieve steady-state performance improvement, a null vector along with the active vector obtained from conventional MPCC is applied during one control period. Two methods are proposed to achieve optimal vector selection and vector duration. The first one requires six predictions and the calculation of current differentiation, while the second one only requires one prediction to obtain the best voltage vector and its optimal duty can be obtained in a very efficient way. The proposed methods are comparatively studied and compared to conventional MPCC and deadbeat control with space vector modulation. Both simulation and experimental results confirm the effectiveness of the proposed methods in achieving good steady-state performance while maintaining quick dynamic response.

Crowdsensing applications utilize the pervasive smartphone users to collect large-scale sensing data efficiently. The quality of sensing data depends on the participation of highly skilled users. To motivate these skilled users to participate, they should receive enough rewards for compensating their resource consumption. Available incentive mechanisms mainly consider the truthfulness of the mechanism, but mostly ignore the issues of security and privacy caused by a “trustful” center. In this paper, we propose a privacy-preserving blockchain incentive mechanism in crowdsensing applications, in which a cryptocurrency built on blockchains is used as a secure incentive way. High quality contributors will get their payments that are recorded in transaction blocks. The miners will verify the transaction according to the sensing data assessment criteria published by the server. As the transaction information can disclose users’ privacy, a node cooperation verification approach is proposed to achieve$k$-anonymity privacy protection. Through theoretical analysis and simulation experiments, we show the feasibility and security of our incentive mechanism.

High recoverable energy density ( W rec ∼ 2.1 J/cm 3 ) was obtained in (0.7 – x )BiFeO 3 -0.3BaTiO 3 - x Bi(Zn 2/3 Nb 1/3 )O 3 + 0.1 wt % Mn 2 O 3 (BF-BT- x BZN, x = 0.05) lead-free ceramics at <200 kV/cm. Fast discharge speeds (<0.5 μs), low leakage (∼10 –7 A/cm 2 ), and small temperature variation in W rec (∼25% from 23 to 150 °C) confirmed the potential for these BiFeO 3 -based compositions for use in high energy density capacitors. A core–shell microstructure composed of a BiFeO 3 -rich core and BaTiO 3 -rich shell was observed by scanning and transmission electron microscopy which may contribute to the high value of energy density. In addition, for x = 0.005, a large electromechanical strain was observed with S pos = 0.463% and effective d 33 * ∼ 424 pm/V, suggesting that this family of ceramics may also have potential for high strain actuators.

In this paper, we propose a discriminative representation for patterned fabric defect detection when only limited negative samples are available. Fabric patches are efficiently classified into defectless and defective categories by Fisher criterion-based stacked denoising autoencoders (FCSDA). First, fabric images are divided into patches of the same size, and both defective and defectless samples are utilized to train FCSDA. Second, test patches are classified through FCSDA into defective and defectless categories. Finally, the residual between the reconstructed image and defective patch is calculated, and the defect is located by thresholding. Experimental results demonstrate the effectiveness of the proposed scheme in the defect detection for periodic patterned fabric and more complex jacquard warp-knitted fabric.

We demonstrate a simple approach for fabricating novel free-standing magnetic and conductive graphene hybrid papers that present great potential as thin layers with effective shielding performance.

Lead-based electroceramics such as Pb(Zr.Ti)O 3 (PZT) and its derivatives have excellent piezoelectric, pyroelectric and energy storage properties and can be used in a wide range of applications. Potential lead-free replacements for PZT such as potassium sodium niobate (KNN) and sodium bismuth titanate (NBT) have a much more limited range of useful properties and have been optimized primarily for piezoelectric applications. Here, we review the initial results on a new generation of lead-free electroceramics based on BiFeO 3 -BaTiO 3 (BF-BT) highlighting the essential crystal chemistry that permits a wide range of functional properties. We demonstrate that with the appropriate dopants and heat treatment, BF-BT can be used to fabricate commercially viable ceramics for applications, ranging from sensors, multilayer actuators, capacitors and high-density energy storage devices. We also assess the potential of BF-BT-based ceramics for electrocaloric and pyroelectric applications.