Search

Metal-Ferroelectric-Metal (MFM) devices possessing high remanent polarizations ( $2P_{r}$ ) of 84 and $73~\mu $ C/cm2 are demonstrated with nearly epitaxially grown Hf0.5Zr0.5O2 (HZO) films on (001) n+-Si(3E19/cm $^{3}$ ) and n+-Ge(3E20/cm $^{3}$ ) substrates, respectively, which are higher than MFM devices with HZO films grown on amorphous SiO2 and partially crystallized TiN underlayers/substrates. The HZO superlattice films by sequential ZrO2/HfO2 plasma-enhanced atomic layer deposition (PEALD) process show high crystallinity in TEM images of all devices; however, the measured $2P_{r}$ values are quite different, ranging from 84 to $33\mu $ C/cm2. The high-resolution scanning transmission electron microscopy (HR-STEM) images of HZO films on n+-Si and n+-Ge show the polarization axis of o-phase is well-aligned with the growth direction which is consistent with observed high $2P_{r}$ values. Much lower interfacial energy at o-phase/Si(Ge) interfaces than m-(t-)phase/Si(Ge) by density functional theory (DFT) calculations indicates that o-phase is greatly stabilized in the HZO films on n+-Si(Ge) substrates. Strong $2P_{r}$ of 51 and $47~\mu $ C/cm2 are measured after 1E9 and 1E11 endurance cycles for HZO films on n+-Si and n+-Ge substrates, respectively. This study shows epitaxial ferroelectric HZO films could be achieved by using small misfit substrates with the thermal budget as low as 450°C.

Global warming, driven by excessive carbon emissions, harms nature and society. Governments’ carbon reduction policies are crucial for sustainable industrial growth, integrating eco-friendly practices into production decisions. In addition, sellers often demand upfront payment for goods as a performance guarantee, receive a portion upon delivery, and finalize the transaction on credit. This advance-cash-credit (ACC) payment scheme is becoming increasingly common. Based on the above, this study proposes a production–inventory model with a carbon cap-and-trade policy and an ACC payment scheme and discusses optimal production and replenishment strategies. We also consider deteriorating raw materials, deteriorating finished products, and an imperfect production system. Mathematical methods and numerical examples are discussed to clarify the solution process, and a sensitivity analysis of parameters is performed. We demonstrate that under the two-stage trade credit and ACC scheme, a larger credit payment component enables the production–inventory system to generate higher total profits, and that a lower charged interest, higher earned interest, or later overall payment (for the ACC scheme) leads to lower carbon emissions. Furthermore, changing carbon emission quota does not affect annual carbon emissions but increasing carbon trading price does, thereby encouraging companies to reduce annual carbon emissions under a carbon cap-and-trade policy.

Cross–modal hashing has gained significant attention due to its efficient computational capabilities and impressive retrieval performance. Most supervised methods rely on the auxiliary learning of a similarity matrix, which incurs computational and storage expenses with a complexity of $O(n^{2})$ . By capturing the adjacency relationships between anchor points and original data, the anchor graph learning strategy effectively reduces the time complexity. However, existing anchor graph hashing methods adopt heuristic sampling strategies like k–means or random sampling to determine anchor points. Unfortunately, this approach separates from the anchor graph construction and fails to accurately capture the fine–grained similarity relationships. To overcome this limitation, we introduce a novel method called supervised consensus anchor graph hashing (SCAGH) for cross–modal retrieval with linear complexity. In SCAGH, the anchor points are automatically selected and consensus anchor graph learning is integrated in an unified framework. Through mutual collaboration, a more fine–grained and discriminative consensus anchor graph can be obtained without extra hyper–parameters. Additionally, we utilize anchor graph matrix to approximate the pairwise similarity matrix so that the high complexity can be avoided and enhance the quality of hash codes. Extensive experiments on four benchmark datasets are conducted to verify the superiority of the proposed SCAGH compared to several state–of–the–art methods.

Conventionally, the single-stage grid-connected PV inverter needs a large PV-side electrolytic capacitor to suppress the double-line frequency current ripple to keep the PV operating at maximum power point (MPP). However, the short lifetime electrolytic capacitor will reduce the PV inverter’s reliability dramatically. In order to overcome the above problem, a novel battery current-sharing power decoupling (BCSPD) method for hybrid photovoltaic (PV) power systems is proposed in this paper. The proposed BCSPD circuit is parallelly connected with the string PV module to achieve as a single-stage topology. Thus, a high power conversation efficiency can be obtained. The current-injection method is adapted to solve the current ripple problem. Therefore, the required capacitance in PV side can be greatly reduced, so long-life film capacitors can be used instead of electrolytic capacitors. In addition, the battery storage system with the droop control is also used to realize the power regulation function to meet the requirements of actual applications. A 1200 W prototype was designed and implemented to assess the system performance. Experimental results show that the proposed system can track MPP, regulate the load power condition, and reduce current ripple.

With the rapid advancement of technology, IoT has become inseparable from human lives. IoT is extensively used in transport, healthcare, and manufacturing, among other sectors. However, this technology lacks sufficient security defense capabilities, thus becoming a highway for malicious actors. IoT networks use infrastructure-based (INF) and device-to-device (D2D) communications to propagate data. The INF communication utilizes technologies such as WLAN, LTE, GPRS, and GSM to relay information from source to destination. The D2D paradigm, on the other hand, is a close-proximity communication in which sensors exchange data in a multi-hop manner. Since malware can utilize both D2D and INF links to spread out, IoT networks are exceptionally vulnerable to attacks. Therefore, we propose Susceptible-Exposed-Infected-Recovered-Dead (SEIRD) model to examine the dynamics of IoT malware spread via INF and D2D communications. We analyze the impacts of mobility on infection propagation and illustrate that our model adequately captures IoT malware spread behaviors through mathematical analysis and simulations. We also compute the malware transmission threshold, which can be used as a guideline to mitigate and suppress an attack.

This paper presents a 1:2 wideband CMOS balun, and its application for sub-6 GHz wideband front-end. In order to eliminate the amplitude/phase mismatch, the primary and secondary windings are shorted by a transmission line (T-line) for a common ground. The float metal in the primary winding is used for artificial dielectric compensation which further reduce the amplitude imbalance by up to about 0.5 dB. A 2.0-to-3.7 GHz wideband RF front-end is also designed by using the proposed CMOS balun. The balun and RF front-end are fabricated in a 0.13- $\mu \text{m}$ bulk CMOS technology. The bandwidth of the balun with $\vert \text {S}11\vert < {-}10$ dB is 2.2-to-5.1 GHz. The fractional bandwidth is large than 79.5%. The corresponding maximum amplitude and phase mismatch is 1.5 dB and 2°, respectively. The measured insertion loss is 4.8-to-5.6 dB from 2.2 to 5.1 GHz.

The steady-state error and torque ripple caused by the mismatch or variation of parameters in the deadbeat predictive current control of permanent magnet synchronous motor are presented in this paper. An improved adaptive deadbeat current predictive model only related to inductance parameters is proposed. The influence of inductance variation on the system stability margin is quantitatively analyzed. When the variation is less than 50%, the system performance can be improved obviously. When the variation reaches 50%, the system will oscillate and cannot operate stably. The feed forward control strategy is introduced to improve the stability margin of the system, and the inductance disturbance of the system oscillation is expanded to 67%. Simulations and experiments are carried out for traditional deadbeat current predictive model, adaptive deadbeat predictive model, and adaptive model with feed forward control. Results show that under various working conditions, the effectiveness of the proposed method in eliminating the steady-state error caused by parameter disturbances is verified. The current distortion and torque ripple are also restrained. Both robustness and steady-state performance of the system are improved.

Many facial landmark methods based on convolutional neural networks (CNN) have been proposed to achieve favorable detection results. However, the instability landmarks that occur in video frames due to CNNs are extremely sensitive to input image noise. To solve this problem of landmark shaking, this study proposes a simple and effective facial landmark detection method comprising a lightweight U-Net model and a dynamic optical flow (DOF). The DOF uses the fast optical flow to obtain the optical flow vector of the landmark and uses dynamic routing to improve landmark stabilization. A lightweight U-Net model is designed to predict facial landmarks with a smaller model size and less computational complexity. The predicted facial landmarks are further fed to the DOF approach to deal with the unstable shaking. Finally, a comparison of several common methods and the proposed detection method is made on several benchmark datasets. Experimental evaluations and analyses show that not only can the lightweight U-Net model achieve favorable landmark prediction but also the DOF stabilizing method can improve the robustness of landmark prediction in both static images and video frames. It should be emphasized that the proposed detection system exhibits better performance than others without requiring heavy computational loadings.

This paper aims to stabilize a switched linear system which transmits its feedback information through digital communication networks. It investigates the effects of three factors on stabilization of the concerned system, including bounded network delay, unknown switching time instants and limited information to represent the system state. The network delay cannot be precisely known, may degrade the system's performance and even destabilize it. Compared with the earlier work on delay-free switched linear systems, it needs to place stricter restrictions on the feedback data rate conditions and the average dwell time. It assumes that the switching is slow enough in the sense of combined dwell time and average dwell time, each individual mode is stabilizable and the available feedback data rate is high enough, but still finite. It proposes some encoding and control strategies to stabilize the switched linear systems with bounded network delay under some feedback data rate conditions.

The integration of Internet of things (IoT) and intelligent transportation system (ITS) is expected to improve the traffic efficiency and enhance the driving experience. However, due to the dynamic traffic environment and various types of vehicles, it is a challenge to perform vehicle classification and speed estimation with a single magnetic sensor. In this paper, based on a single low-cost magnetic sensor, a scheme is proposed to achieve vehicle classification and speed interval estimation by designing a two-dimensional convolutional neural network (CNN). Specifically, we extract the magnetic field data of each vehicle and then convert the collected data into a two-dimensional grayscale image. In this way, the images of vehicle signals with different types and driving speeds can be used as the input data to train the designed CNN model. With the designed CNN model, we classify the vehicles into 7 types and estimate the speed interval of each vehicle, where the speeds in the range of 10km/h-70km/h are divided into 6 intervals of size 10km/h. The performance of the proposed vehicle classification and speed estimation scheme is evaluated by experiments, where the experimental results show that the accuracy of vehicle classification and the accuracy of speed interval estimation are 97.83% and 96.85%, respectively.

Vector reduction is a very common operation to reduce a vector into a single scalar value in many scientific and engineering application scenarios. Therefore a fast and efficient vector reduction circuit has great significance to the real-time system applications. Usually the pipeline structure is widely adopted to increase the throughput of the vector reduction circuit and achieve maximum efficiency. In this paper, to deal with multiple vectors of variable length in random input sequence, a novel tag based fully pipelined vector reduction circuit is firstly proposed, in which a cache state module is used to queer and update the cache state of each vector. However, when the quantity of the input vector becomes large, a larger cache state module is required, which consumes more combinational logic and lower the operating frequency. To solve this problem, a high speed circuit is proposed in which the input vectors will be divided into several groups and sent into the dedicated cache state circuits, which can improve the operating frequency. Compared with other existing work, the prototype circuit and the improved circuit based on the prototype circuit can achieve the smallest Slices × us (

Image edge detection in virtual reality scenes is one of the most important technologies in the field of image processing and computer vision. It occupies an important position in the image processing system and is a key factor that affects the performance of the entire system. The quality of the algorithm directly affects the performance of the computer's visual system. This paper briefly describes the basic theoretical methods of image edge detection. The basic theory and processing flow of image edge detection are analyzed, and several commonly used edge detection preprocessing methods and their basic principles are elaborated in detail. The existing traditional edge detection methods are briefly introduced, and the traditional edge detection methods are compared. This paper studies the adaptive multi-scale edge detection method based on Canny algorithm, and through theoretical analysis, compares the advantages and disadvantages of various algorithms in image edge detection. In the process of acquiring image edges, the local maximum value of the modulus is calculated along the gradient direction and the threshold is adaptively selected based on the image blocking principle. Comparing with the traditional modulus maximum edge detection method, this algorithm overcomes the contradiction that can suppress the interference of noise and obtain better edge detection effect to a certain extent. Experiments show that the method in this paper not only suppresses the interference of impulsive noise to the image edge detection, but also greatly reduces the possibility of false edges, and obtains a satisfactory accurate binary edge image.

A neural network model based on deep learning is utilized to explore the traffic sign recognition (TSR) and expand the application of deep intelligent learning technology in the field of virtual reality (VR) image recognition, thereby assessing the road traffic safety risks and promoting the construction of intelligent transportation networks. First, a dual-path deep CNN (TDCNN) TSR model is built based on the convolutional neural network (CNN), and the cost function and recognition accuracy are selected as indicators to analyze the training results of the model. Second, the recurrent neural network (RNN) and long-short-term memory (LSTM) RNN are utilized to assess the road traffic safety risks, and the prediction and evaluation effects of them are compared. Finally, the changes in safety risks of road traffic accidents are analyzed based on the two key influencing factors of the number of road intersections and the speed of vehicles traveling. The results show that the learning rate of the network model and the number of hidden neurons in the fully-connected layer directly affect the training results, and there are differences in the choices between the early and late training periods. Compared with RNN, the LSTM network model has higher evaluation accuracy, and its corresponding root square error (RSE) is 0.36. The rational control of the number of intersections and the speed of roads traveled has a significant impact on improving the safety level and promoting road traffic efficiency. The VR image recognition algorithm and safety risk prediction method based on a neural network model positively affect the construction of an intelligent transport network.

Internet of Things (IoT) is an emerging network that aims to connect massive devices for exchanging information with each other. There have been various IoT access techniques, such as RFID, Blue Tooth, WiFi, LoRa and cellular IoT, in which cellular IoT has advantages in wide coverage and the economical reuse of exsiting cellular infrastructure. However, cellular base station (BS) consumes most of the energy in wireless communication networks. Therefore, improving the energy efficiency (EE) of the BS is of great importance to make cellular IoT green. In this paper, we propose a novel orbital angular momentum (OAM)-based concentric spatial division multiplexing (CSDM) downlink transmission scheme for cellular IoT terminals. Specifically, the hybrid radio frequency (RF) and baseband CSDM scheme exploits analog OAM beamforming to generate concentric annular beams for covering cell-center, cell-mediate and cell-edge groups of users, and digital space-frequency vector perturbation (SFVP) precoding and orthogonal frequency division multiple access (OFDMA) to mitigate inter- and intra-group interferences. Mathematical analysis and numerical simulations validate that the IoT BS with the proposed CSDM-SFVP scheme has better bit error rate (BER) and EE performance than traditional massive multiple-input multiple-output (MIMO) BS with full-baseband zero-forcing (FBZF) precoding in a IoT cell.

As one of the most successful recommendation techniques, collaborative filtering provides a useful recommendation by associating an active user with a crowd of users who share the same interests. Although some achievements have been achieved both in theory and practice, the efficiency of recommender systems has been negatively affected by the problems of cold start and data sparsity recently. To solve the above problems, the trust relationship among users is employed into recommender systems to build a learning model to further promote the prediction quality and users' satisfaction. However, most of the existing social networks-based recommendation algorithms fail to take into account the fact that users with different levels of trust and backgrounds, that is, user's social status and homophily have different degrees of influence on their friends. In this paper, a novel social matrix factorization-based recommendation method is proposed to improve the recommendation quality by fusing user's social status and homophily. User's social status and homophily play important roles in improving the performance of recommender systems. We first build a user's trust relationship network based on user social relationships and the rating information. Then, the degree of trust is calculated through the trust propagation method and the PageRank algorithm. Finally, the trust relationship is integrated into the matrix factorization model to accurately predict unknown ratings. The proposed method is evaluated using real-life datasets including the Epinions and Douban datasets. The experimental results and comparisons demonstrate that the proposed approach is superior to the existing social networks-based recommendation algorithms.

In this paper, an improved brain storm optimization (BSO) algorithm is proposed to solve the optimization problem in a hybrid renewable energy system. The objective of the proposed algorithm is the minimization of the annualized costs of the system (ACS), the loss of power supply probability (LPSP), and the total fuel emissions. In the proposed algorithm, first, the K-Means clustering method is embedded to make the same clusters have similar solutions. Then, the distance of a city block is taken as the distance measure, which makes the solution feasible. Then, to measure the merits and demerits of each individual, the composite index is utilized as the fitness value. In addition, to improve the efficiency of the algorithm, a pair of crossover and mutation strategies are designed in detail. Finally, a set of realistic instances are used to test the performance of the proposed algorithm, and after detailed experimental comparisons, the competitive performance of the proposed algorithm is verified.

Although using the mobile user interface pattern (MUIP) can increase the efficiency and quality of mobile interface development, the existing pattern search methods have some limitations to provide appropriate patterns for solving a given design problem. In order to find the appropriate patterns, a category theory-based MUIP recommendation (CTR) method is proposed in this paper. First, the MUIP search is represented with a categorical structure so that the design problem can combine with the hierarchical structural MUIP repository closely. Then, to extract more accurate requirement information from the design problem, we present a question-based information extraction method that combines the general architecture for text engineering framework and category theory to extract the design goal, context, and level from the design problem. Finally, based on the categorical structure, we adopt a corresponding search strategy to find MUIPs according to the type of design problem. Some experiments are conducted to evaluate the performance of CTR. The experimental results show that the CTR can provide a high performance in terms of accuracy and ranking when compared with the existing methods.

This study Development a high-speed data acquisition (DAQ) device by using AD9226 analog-to-digital converters, a field programmable gate array, and an ARM Cortex-A8 microprocessor for a self-designed synchronous 6-channel high-speed DAQ card that was able to transmit data to a computer through its network interface. Its cost was approximately 10% that of a commercial model, the National Instruments PXI-5105, and thus overcame the prohibitively high cost of commercial DAQ cards. A high-frequency current transformer (HFCT) was used to measure three types of typical partial discharge (PD) in self-made models to compare the performance of the self-designed DAQ card and that of the National Instruments PXI-5105. The HFCT signals were converted into three-dimensional PD patterns, and mean discharge was chosen as the feature to be extracted for the application of extension theory in the recognition of discharge models. The results revealed that the self-designed DAQ card was comparable to the commercial model in the recognition of high-frequency PD signals. Given the high price of commercial high-speed DAQ devices, the self-designed DAQ card was deemed to have considerable advantages in cost and expandability.

Fault location in a power distribution network is a challenging task due to the presence of multilayer branches and short line lengths. Existing fault-location methods generally require measurements at both ends of each branch, which requires a large number of measuring points. The placement of measuring points at branch terminals is an approach that can be used to reduce the number of measuring points. Such a measuring point layout allows the existing fault-location methods for power distribution networks to determine fault points after identifying faulted branches. However, these methods fail to locate a fault if the faulted branch cannot be correctly identified. This paper proposes a traveling-wave-based fault-location method for branched power distribution networks without requiring faulted branches to be identified. In the proposed method, by using the first arrival times of the fault-generated traveling waves detected at the substation and each branch terminal, the computational fault time difference (CFTD) is defined. By calculating the value of CFTD, the fault point is directly searched out. Finally, the quartile method is used to eliminate the impact of the arrival-time error on the fault-location accuracy of the proposed method. The simulation results verify the high accuracy, traveling-wave velocity stability, and strong arrival-time error robustness of the proposed method.

System capacity and service coverage are the most critical performance metrics in cellular wireless communication networks. Usually, system capacity enhancements are at the expense of service coverage degradations, and vice versa. This capacity-coverage tradeoff and the associated joint optimization problem becomes very challenging in massive multiple-input multiple-output (MIMO) wireless systems, due to a large amount of antenna tilt values to be configured and very sophisticated inter-cell interference conditions, under massive antenna scenarios. This paper proposes a novel approach, namely group alignment of user signal strength (GAUSS), to efficiently support the user scheduling for the massive MIMO system, and thus serve as an effective parameter for the coverage and capacity optimization (CCO) problem. Together with a unified threshold of Quality of Service, i.e. the minimum signal-to-interference-plus-noise ratio (SINRmin) for user satisfaction, GAUSS can effectively control the variance of signal strengths of multiple users in the neighborhood. Moreover, an intelligent and efficient deep-learning enabled coverage and capacity optimization (DECCO) algorithm is proposed and evaluated, which adopts a pre-trained deep policy gradient-based neural network to dynamically derive GAUSS and SINRmin during CCO. Furthermore, an inter-cell interference coordination (ICIC) is proposed to enhance the CCO performance. Analytical and simulation results show that the proposed DECCO algorithm can effectively achieve a much better performance balance between system capacity and service coverage than traditional fixed optimization (FO) and proportional fair optimization (PFO) algorithms. Specifically, DECCO significantly increases the overall spectrum efficiency by 24% and 40%, respectively, than FO and PFO in a typical massive MIMO system.

Because insulators provide electrical insulation and mechanical support for electric transmission lines, these components are of paramount importance to safe and reliable operations of power systems. However, insulators are often considered to be prone to different faults, e.g., bunch-drop, which demands a novel solution for accurate fault detection and fault location. Current research efforts have primarily focused on the bunch-drop fault of glass insulators, and the study of ceramic insulators has not been reported to date. To this end, this paper proposes an algorithmic solution for the bunch-drop fault detection for both glass and ceramic insulators based on spatial morphological features, which can be integrated into an unmanned aerial vehicle-based inspection system. Color models can be established based on the unique color features of both glass and ceramic insulators. Next, the target areas of the insulators can be identified according to the color determination combined with the insulator's spatial features. The target area is morphologically processed to highlight the fault location, and the rules are established based on the spatial feature differences between the insulators with and without faults. Consequently, the fault location can be accurately identified, and the coordinates can be determined. The performance of the proposed solution is evaluated in comparison with existing solutions. The numerical results demonstrate that the proposed solution can detect the bunch-drop faults of insulators with a better than average detection rate. In addition, the performance is assessed and validated in terms of robustness and real-time performance.

As the number of antennas goes large in the massive multiple-input multiple-output (MIMO) scenario, the large-scale fading plays an increasingly important role in the system performance. In this paper, we investigate the uplink sum rate of a massive distributed MIMO system over composite fading channels portrayed by the Rayleigh-Lognormal (RLN) model, where both zero forcing (ZF) and ZF decision-feedback receivers are considered. By exploring a novel and generalized method based on the integral and limit theories, the sum rate is expressed as a series with regard to the negative power of the transmit antennas, which is followed by a brief discussion on the asymptotic behavior when the number of transmit antennas goes infinite. Numeral experiments testify our theoretical analysis and prove the effectiveness even when the accuracy parameter is small. The influence of the number and location of the radio ports is also discussed.

In the era of big data, recommender system (RS) has become an effective information filtering tool that alleviates information overload for Web users. Collaborative filtering (CF), as one of the most successful recommendation techniques, has been widely studied by various research institutions and industries and has been applied in practice. CF makes recommendations for the current active user using lots of users' historical rating information without analyzing the content of the information resource. However, in recent years, data sparsity and high dimensionality brought by big data have negatively affected the efficiency of the traditional CF-based recommendation approaches. In CF, the context information, such as time information and trust relationships among the friends, is introduced into RS to construct a training model to further improve the recommendation accuracy and user's satisfaction, and therefore, a variety of hybrid CF-based recommendation algorithms have emerged. In this paper, we mainly review and summarize the traditional CF-based approaches and techniques used in RS and study some recent hybrid CF-based recommendation approaches and techniques, including the latest hybrid memory-based and model-based CF recommendation algorithms. Finally, we discuss the potential impact that may improve the RS and future direction. In this paper, we aim at introducing the recent hybrid CF-based recommendation techniques fusing social networks to solve data sparsity and high dimensionality and provide a novel point of view to improve the performance of RS, thereby presenting a useful resource in the state-of-the-art research result for future researchers.

Computer aided detection and diagnosis system (CAD) is designed to improve the efficiency and quality of medical diagnosis. The existing CAD system tries to realize a diagnosis process in an automatic way to replace the doctor's diagnosis work completely, but it is difficult to capture the doctor's detection and diagnosis needs and goals accurately due to lack of doctor's experience. Thus, the accuracy is not up to the doctor's manual diagnosis results, and it is a tedious and cumbersome job to use the system because it requires manual intervention, which leads to the fact that CAD system was difficult to be widely used. This paper analyzes the characteristics of doctors' diagnosis tasks deeply, and makes the reasonable allocation of tasks between the doctor and computer. Then the perceptual control theory is introduced into the task model, a PTT task model is proposed to make it express uncertainty of the doctor's diagnosis tasks, and it can be used as a reference for future uncertainty task analysis. Therefore, an interactive CAD tasks framework is put forward with the assistant decision support provided for doctors by means of Bayesian network model and conceptual graph semantic matching method to diagnose tasks. In addition, effective support is provided for interface development staffs to design interactive systems that are suitable for the physicians' diagnostic tasks. The experimental results show that the method not only improves the accuracy and efficiency of the medical diagnosis, but also improves the usability of the system design.