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The installed capacity of renewable energy has continued to increase in recent years, causing excess power to be transmitted to neighboring provinces by DC transmission with wind and thermal power bundling. However, with increasing coal cost and decreasing investment benefit of thermal power, the economic benefit of thermal power investment has decreased. Therefore, this paper proposes a method that replaces the thermal power unit with renewable energy to assume the peak regulating function of the power system. First, a novel heat storage strategy for concentrating solar power (CSP) stations is established. On this basis, the CSP station is included in the wind-thermal bundling model to replace the peak regulating function of some thermal power units, and the planning method of wind-thermal-CSP bundling with an adjustable heat storage strategy is developed. By adjusting the heat storage strategy and comparing the economic parameters under the different strategies, the optimal ratio of the installation of different powers is obtained.

Due to the high dependence of economic and social development on power systems, the demand for reliable operation of power systems is increasing. Considering the popularity and widespread installation of smart meters, accurate system/node reliability indexes can be obtained. The inverse problem of reliability evaluation (IPRE) refers to the use of known system/node reliability indexes to obtain component reliability parameters. In this paper, a novel method of solving the IPRE is proposed. First, based on a nonsequential Monte Carlo (NSMC) method, analytical expressions for system reliability indexes in terms of component reliability parameters are derived, and then, the nonlinear equations of the IPRE are constructed. Second, a high-order polynomial approximation based on the conjugate gradient algorithm is used to calculate the unknown component reliability parameters, and the results are compared with those obtained using traditional neural networks method. Finally, a continuation method is used to correct the errors of the obtained component reliability parameters. Three cases, namely, the IEEE 1979 Reliability Test System (IEEE RTS-79), the Roy Billinton Test System (RBTS) and the Chuanyu power system in Southwest China, are used to test the method proposed in this paper to verify its feasibility and accuracy.

The bird's nest on the transmission line tower has a bad impact on the transmission equipment, and even threaten the safe and stable operation of the power grid. In recent years, the number of bird pest in transmission line is increasing year by year, resulting in increasing economic losses. The traditional bird's nest identification method of transmission line is time-consuming and labor-intensive, and its security level is low. Therefore, this paper proposes an automatic detection method of bird's nest on transmission line tower based on Faster_RCNN convolution neural network. This method can automatically identify the location of the bird's nest on the transmission line tower by using the image collected by unmanned aerial vehicle (UAV). The problem of insufficient training samples and overfitting of neural network classifier is solved by enlarging the bird's nest image. The experimental results show that this method can effectively detect bird's nest targets in complex environment, and the highest recall rate can reach 95.38%, the highest F1 score can reach 96.87%, and the detection time of each image can reach 0.154s. Compared with the traditional nest detection method, this method has stronger applicability and generalization ability. It provides technical support for analyzing bird activities and taking effective preventive measures.

Sentiment analysis is one of the significant application fields of natural language processing. Researchers divide the sentiment into explicit sentiment and implicit sentiment according to whether the expression contains sentiment words or not. Most of the existing research focuses on explicit sentiment tasks. However, implicit sentiment analysis is difficult to study because of its implicit tendency to express sentiment and the problem of “weak features” and “multi-confounding weak features”. We develop a novel model based on “Hierarchical Knowledge Enhancement and Multi-pooling”(HKEM), which fully integrates the knowledge information of different levels in the text through hierarchical knowledge enhancement to alleviate the “weak feature” problem. At the same time, the multi-pooling method is used to extract and analyze multiple features, and the “multi-confounding weak features” are modeled. In this paper, comparative experiments have been conducted on the SMP-ECISA2019 dataset, and the results show that the accuracy of this model is comparable to that of the current best model, and the F1 score is 5.9% higher than that of the best model, indicating that the model proposed in this paper can accurately analyze Chinese implicit sentiment and achieve the state-of-the-art in the comprehensive performance evaluation, which proves the effectiveness and superiority of the model in this paper.

With the rapid development of communication technology, a large amount of spatiotemporal trajectory data has been produced. One of the critical applications of trajectory data is location prediction that is important for urban traffic planning and location-based services. Although many methods for personal location prediction have been proposed, to the best of our knowledge, some users always have sparse historical trajectory data in practical applications, resulting in poor location prediction precision. Targeting on this challenge, we propose an Individual Trajectory-Group Trajectory (ITGT) location prediction model by utilizing the pattern of group travels. First, the model performs the stay point extraction and conducts the spatial clustering to construct the clustering link. Second, Fano's inequality and clustering link are used to evaluate the predictability of location. Third, two variable order Markov models, named prediction by partial match (PPM) and probabilistic suffix tree (PST), are adopted to predict the clustering link. Finally, our approach is evaluated by using 608 712 points from 5000 volunteers at Shenzhen, China. The results show that: 1) when using individual trajectory, the PPM individual model is superior to the conventional N-order Markov model and PST individual model; 2) when all group trajectories are used, the PPM group model is not as accurate as the PPM individual model; and 3) when it classifies the group trajectory into different traffic zones, the PPM zone model is better than the PPM group model and the PPM individual model. The prediction precision of 1-3 order PPM zone model is 83.21%, 86.75%, and 87.35%, respectively, which introduces approximately 3% performance gains by utilizing the characters of traffic zone groups.

Given the growth, complexity, and size of the Internet, new methodologies are needed to support cost-effective resource provisioning. This paper provides a cost-based polynomial-time heuristic algorithm for resource provisioning optimization called multi-layered market algorithm (MMA). The MMA is solvable for multi-layered, multi-technology, and practical-sized networks, where the traffic is modeled as a combination of constant bit-rate and variable bit-rate (VBR) traffic streams. A VBR stream is modeled either by a Gaussian process or by a Poisson Pareto burst process (PPBP) which under certain parameter values is long-range dependent – a known characteristic of the Internet traffic streams. The consideration of VBR traffic models in a multi-layered network optimization is a key novel aspect of MMA. The MMA considers a range of transport technologies operating in layers and traffic sharing schemes. The MMA implements flow-size-based routing where flows according to their sizes are routed independently. As routing affects resource requirement, such considerations are important for resource provisioning by the given cost models. The complexity resulting from these considerations, including layering and PPBP traffic, requires a simplified design philosophy which in this paper, is based on adopting the shortest path routing in each layer. This is achieved by MMA which is based on an iterative algorithm, and resource provisioning that is performed link-by-link in all layers. As a benchmark for MMA, we provide an integer linear programming (ILP) formulation for a multi-layered network optimization problem with fixed end-to-end demands. The MMA is validated by comparing its solutions to those ILP results in different variants of a six-node network, and its software is verified using double-entry bookkeeping – a method commonly used in accounting systems. The MMA runs on a platform called network mark-up language, which enables visualization and further validation of the results.

Compared to half-duplex (HD) communications, full-duplex (FD) communications have the potential to double the throughput of the cellular networks. However, the inter-user interference (IUI) from the uplink (UL) users to the downlink (DL) users severely hinders the improvement of the throughput. In this paper, we propose an interference neutralization (IN) scheme to eliminate the IUI with the help of partial channel state information at the transmitter (CSIT). Before the UL signals are transmitted, we rotate their phases through a set of scalars known by the base station (BS), which endows the conventional receive antennas of the BS with the reconfigurability. The BS assists the IUI management by precoding and forwarding the UL signals to the DL users. Consequently, the UL signals forwarded by the BS locate in the opposite direction of the IUI so as to neutralize it. After the IUI is neutralized, all the symbols of the last time slots can be recovered. The DoF region and the corresponding feasible conditions are analyzed. The analysis and numerical results demonstrate that 1.5-fold DoF over that of the HD systems can be achieved via the proposed IN scheme with only partial CSIT and 2 symbol extensions. It shows that the proposed scheme is a low complexity interference management scheme for the FD cellular networks.

The method of text sentiment analysis based on sentiment dictionary often has the problems that the sentiment dictionary doesn't contain enough sentiment words or omits some field sentiment words. In addition, due to the existence of some polysemic sentiment words with positivity, negativity, and neutrality, the words' polarity cannot be accurately expressed, so the accuracy of text sentiment analysis is reduced to some extent. In this paper, an extended sentiment dictionary is constructed. The extended sentiment dictionary contains the basic sentiment words, the field sentiment words, and the polysemic sentiment words, which improves the accuracy of sentiment analysis. The naive Bayesian classifier is used to determine the field of the text in which the polysemic sentiment word is. Thus, the sentiment value of the polysemic sentiment word in the field is obtained. By utilizing the extended sentiment dictionary and the designed sentiment score rules, the sentiment of the text is achieved. The experimental results prove that the proposed sentiment analysis method based on extended sentiment dictionary has certain feasibility and accuracy. The research is meaningful for the sentiment recognition of the comment texts.

Blind image quality assessment (BIQA) methods aim to predict quality of images as perceived by humans without access to a reference image. Recently, deep learning methods have gained substantial attention in the research community and have proven useful for BIQA. Although previous study of deep neural networks (DNN) methods is presented, some novelty DNN methods, which are recently proposed, are not summarized for BIQA. In this paper, we provide a survey covering various DNN methods for BIQA. First, we systematically analyze the existing DNN-based quality assessment methods according to the role of DNN. Then, we compare the prediction performance of various DNN methods on the synthetic databases (LIVE, TID2013, CSIQ, LIVE multiply distorted) and authentic databases (LIVE challenge), providing important information that can help understand the underlying properties between different DNN methods for BIQA. Finally, we describe some emerging challenges in designing and training DNN-based BIQA, along with few directions that are worth further investigations in the future.

In this paper, we experimentally demonstrated the high-speed low-cost mode-division-multiplexed (MDM) discrete multi-tone (DMT) signal transmission based on cylindrical vector beam (CVB) multiplexing over few-mode fiber (FMF) with intensity modulation and direct detection (IM/DD) utilizing electro-absorption modulated laser (EML). Two CVBs (TM01 and TE01 modes) are employed for signal transmission simultaneously with the minimum mode isolation of 16.7 dB between them after transmission over 5 m 4-mode FMF. The digital discrete Fourier transform-spread (DFT-spread) and pre-equalization techniques are implemented in transmitter to improve the system performance. By adopting the proposed schemes, the 240 Gbit/s DFT-spread DMT signal has been transmitted successfully over 5 m FMF below the bit-error-ratio (BER) of $3.8\times 10^{-3}$ which is the hard-decision forward-error-correction (HD-FEC) threshold. The required received optical powers (ROPs) of the two CVBs (TM01 and TE01 modes) are -7.27 dBm and -7.15 dBm, respectively. The experimental results show that CVB-based multiplexing technology combined with advanced modulation format could be a potential candidate for high-speed low-cost server backplane optical interconnection.

Public transport is vital to people's daily travel, and bus dispatching plays a significant role in the public transport system. With deep learning having been widely applied and achieved great success in many fields, bus dispatching methods based on deep learning are proposed in succession. Currently, many bus dispatching models assume that the bus departure timetable is fixed and optimize the bus departure timetable interval according to passenger flow. However, the bus departure timetable is variable in general, only considering that the bus arrival time is insufficient. Targeting the above challenges, we propose a novel dynamic bus dispatching model based on arrival time and passenger flow prediction (D-ATPF). First, the historical origin-destination (OD) data and the transfer data are obtained by processing the bus trajectory data and the passenger card-swiping records, and the bus arrival time is extracted by analyzing the GPS trajectory. Second, the components of bus arrival time and passenger flow prediction based on long short-term memory (P-LSTM) are adopted to predict the future passenger flow and bus arrival time. Finally, the genetic algorithm-based bus dispatching model (GABD model) searches the minimum waiting time for passengers by using stay strategy. By using data of five lines with 124 bus stations and a total of 902509 records in Guangzhou city, China, our experimental results show that: 1) the average mean absolute percentage error (MAPE) and root mean square error (RMSE) of passenger prediction are 14% and 7.5, respectively; 2) the average MAPE and RMSE of bus arrival time are 7.5% and 13.5, respectively; 3) regarding the passenger flow and arrival time prediction, the proposed D-ATPF model reduced waiting time by 829.68 min, accounting for 25.19% of the total waiting time; and 4) compared with the real-time stay strategy, the reduced waiting time of this method increased by 5.94%. Therefore, the D-ATPF model provided a more practical model for buses dispatching.

Ultrasonogram is one of the main techniques for the non-invasive observation and the diagnosis of the thyroid gland. And, the thyroid papillary carcinoma (TPC) was usually diagnosed during the regular examination of the thyroid gland. The current diagnosis guideline heavily replies on the experienced clinical endoscopists. This paper comprehensively evaluated four classification algorithms and five image feature extraction algorithms for the TPC diagnosis problem. Our data demonstrated that the Hessian features extracted from the transverse ultrasonograms performed better than those from the longitudinal view. The best model (Acc = 0.9949) was achieved by the seven-layer shallow neural network with the LBP and Hessian features extracted from both the longitudinal and transverse views of the ultrasonograms.

In this paper, a minimum-time motion online planning (MTMOLP) method is proposed for underactuated overhead crane systems within both safety and physical constraints. First, the motion process is divided into seven stages by analyzing the coupling between trolley movement and payload swing, and a minimum-time motion planning problem and the expression of its solution are presented. Second, a state prediction method based on the payload swing energy is given to compute the solution of the motion planning problem in real time. Finally, several simulations and experiments are conducted to verify the feasibility and effectiveness of the proposed method. The results show that the MTMOLP method can obtain the solution of the minimum-time motion planning problem with spending very little time on the computing process. Therefore, it can be applied to the online motion planning of the overhead cranes. Furthermore, the motion process division by the coupling analysis can also provide a reference for the underactuated control of overhead crane systems.

In this paper, we propose a novel distributed digital transmission framework for two jointly sparse correlated signals. First, the non-zero coefficients of each signal are quantized by a standard quantizer or a novel distributed quantizer, as appropriate. Then, these quantized values are mapped to the elements of a finite field, except 0, while the zero coefficients are mapped to 0. Subsequently, compressed sensing over finite fields is applied to obtain measurements. We name such an order first quantization then compressed sensing. The two measurement signals are then converted to bit sequences, modulated, and transmitted through separate additive white Gaussian noise (AWGN) channels. At the central receiver, which has access to both channels, following demodulation, an innovative joint belief propagation (JBP) algorithm is performed for joint recovery. In this algorithm, we introduce a new type of constraint node, i.e., correlation constraint nodes, which connect two factor graphs that separately represent the CS encoding matrix of each signal. The experimental results prove that under the same framework the proposed scheme provides significant performance improvements compared to the scheme that ignores the correlated information between jointly sparse signals, especially when the correlation coefficient is high. Then, to answer the question of which order is better, we construct the first compressed sensing, then quantization framework, for fairness, two cutting edge jointly greedy pursuit algorithms are separately adopted at the joint decoder. Through simulations, we validate that the proposed framework provides more effective and robust transmissions.

The mining of network sensitive information is of great significance for understanding the social stability of the network. Obtaining the network public opinion of sensitive information is helpful to master Internet users' attitudes toward important social events. The related artificial intelligence technology can achieve the topics from the network texts. At present, the current topic recognition model has a low recognition rate for sensitive information and usually generates some inaccurate topic keywords. In this paper, a topic recognition method of the network sensitive information based on a sensitive word weighted-latent Dirichlet allocation (LDA) model is proposed. First, the basic sensitive word vocabulary is constructed by manual collection, and the embedding representation of the word is obtained through the training of a large amount of network corpus based onWord2vec. The semantic similarity between the word embedding is calculated to extend the basic sensitive word vocabulary. Second, the extended sensitive word vocabulary is embedded in the LDA model. On the one hand, it can improve the semantic understanding and the recognition ability of LDA for the sensitive topic words and promote the quality of the generated topic words. On the other hand, it can also improve the relevance of the topic keywords and the related topics and find more fine-grained keywords. The experimental results show that the sensitive word weighted-LDA model can effectively improve the topic recognition quantity and quality of sensitive information. This paper is helpful to the development of artificial intelligence. The generated corpus in this paper is meaningful to the research of text classification, clustering and information retrieval, and so on.

In this paper, a K-band antenna with ±60° beam scanning is proposed for a mmWave (millimeter-Wave) radar sensor. Especially, we take advantages of the magnetic dipole principle, and design the antenna by employing via holes in three sides, so as to form a cavity which can be equivalently considered as magnetic dipole, resulting in the 3 dB beam width as wide as 140° in E-plane. We cascade the mmWave antenna to 1×4 and 4×4 arrays for radar transmitting and receiving, respectively. The antenna array is fabricated and implemented to a K-band radar and its wide-angle beam scanning performance is evaluated by the radar's distance and angular detections.