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This paper studies the time-dependent cold chain logistics vehicle routing problem considering both traffic congestion and carbon emissions. A cold chain logistics model with time-dependent green vehicle paths with time windows (TDGVRPTW) was developed to fulfil the demands of green logistics and to take comprehensive account of consideration should be given to factors such as road congestion and carbon emissions. The objective of the model is to minimise total costs, which include carbon emission costs, penalty costs, fuel consumption costs, fixed costs, damage costs and refrigeration costs. Two-phase hybrid search algorithm was developed to solve this model. During the initial stage of the algorithm, a dual-population ant colony optimization (DACO) algorithm sharing the optimal individual is employed to optimize the distribution route of the vehicle. During the second phase, an adaptive golden section search (AGSS) algorithm is used to optimise the departure time of the vehicle from the distribution centre to avoid traffic congestion time periods. To validate the effectiveness of the suggested two-phase hybrid search algorithm, it is applied to the improved Solomon benchmark test set. The experimental findings demonstrate that the two-phase hybrid search algorithm can reasonably plan the driving routes and departure times for each vehicle, effectively avoiding peak traffic congestion periods in the city, and reducing the overall delivery cost.

In this work, an accurate temperature-dependent drain current $I_{\mathrm { D}}$ fluctuation model valid from 10 to 300 K was proposed for 18 nm ultra-thin body and buried oxide (UTBB) n-channel field effect transistors (n-FETs). The temperature dependence of $I_{\mathrm { D}}$ fluctuation was characterized and investigated from 300 K down to 10 K. In moderate inversion mode, $I_{\mathrm { D}}$ fluctuation is more severe at sub-100 K while in the strong inversion mode, it still can be overshadowed by the charge screening effect. Cryogenic virtual source (CVS) device model was used to extract and analyze the carrier density and mobility which are used in the current fluctuation model. The current fluctuation model was experimentally verified under different inversion conditions, showing it can be used to analyze and optimize the flicker noise in the low temperature (LT) circuit applications.

This study presents an effective data-driven anomaly detection scheme for drunk driving detection. Specifically, the proposed anomaly detection approach amalgamates the desirable features of the t-distributed stochastic neighbor embedding (t-SNE) as a feature extractor with the Isolation Forest (iF) scheme to detect drivers’ drunkenness status. We used the t-SNE model to exploit its capacity in reducing the dimensionality of nonlinear data by preserving the local and global structures of the input data in the feature space to obtain good detection. At the same time, the iF scheme is an effective and unsupervised tree-based approach to achieving good detection of anomalies in multivariate data. This approach only employs normal events data to train the detection model, making them more attractive for detecting drunk drivers in practice. To verify the detection capacity of the proposed t-SNE-iF approach in reliably detecting drivers with excess alcohol, we used publically available data collected using a gas sensor, temperature sensor, and a digital camera. The overall detection system proved a high detection performance with AUC around 95%, demonstrating the proposed approach’s robustness and reliability. Furthermore, compared to the Principal Component Analysis (PCA), Incremental PCA (IPCA), Independent component analysis (ICA), Kernel PCA (kPCA), and Multi-dimensional scaling (MDS)-based iForest, EE, and LOF detection schemes, the proposed t-SNE-based iF scheme offers superior detection performance of drunk driver status.

Accurate modulation identification of the received signals is undoubtedly a central component in multiple-input multiple-output (MIMO) communication systems, facilitating the demodulation task. This study presents a flexible and semi-supervised deep learning-driven strategy for automatic modulation identification. To this end, the multiclass classification problem is treated as multiple binary discrimination problems to address modulation identification challenges. Here, we merge the features extraction ability of the Generative Adversarial Network (GAN) model and the semi-supervised anomaly detection scheme, the one-class Support Vector Machine (1SVM). Essentially, a single GAN-based 1SVM detector is trained using training data of each class, with the samples of that class as inlier and all other samples as anomalies (i.e., one-vs.-rest). The 1SVM is trained using the features learned by the GAN model. A dataset consisting of three digital modulations (i.e., BFSK, CPFSK, and PAM4) and three analog modulations (i.e., AM-DSB, AM-SSB, and WB-FM), widely used in wireless communications systems, is employed to demonstrate the performance of the considered deep learning-based methods. Compared to Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN)-based 1SVM, the conventional GAN, DBN, and RBM with softmax layer as discriminator layer, the proposed GAN-based 1SVM detector offers superior discrimination performance of modulation types by achieving an averaged accuracy of 0.951 and F1-Score of 0.954. Results also showed that the GAN-1SVM detector dominates the state-of-the-art modulation classification techniques.

Recent work on alliance partner selection focuses on the relationship between partner features and alliance performance, while ignoring the influence mechanism of alliance partner features on different innovation types of firms. In this paper, based on proximity perspective, the influence mechanism model of alliance partner features on firms’ dual innovation is constructed. Analyzing questionnaires and patent data of high-tech firms, it is found that (1) Technological proximity (TP) has a positive impact on focal firms’ exploratory innovation (EXR) and exploitative innovation (EXI); (2) Knowledge coupling (KC) mediates the relationship between TP and dual innovation; (3) Social proximity (SP) positively moderates the relationship between TP and dual innovation, and positively moderates the relationship between TP and KC; Meanwhile, (4) there is a moderating effect of different levels of SP on the magnitude of the mediating effect of KC. By constructing a theoretical framework based on “proximity – knowledge coupling – dual innovation”, this paper is helpful to draw scholars’ attention to the relationship between proximity and dual innovation, and provide theoretical reference for firms to select alliance partners reasonably and efficiently.

Network embedding usually learns the node representations using their local context information. However, as an important mesoscopic description of network structures, community structures hidden in the networks have been largely ignored. Incorporating community structures into network embedding could provide effective and a wealth of information to overcome the problem of sparse data in local structures, and make the learned node representation more identifiable. Inspired by this, we propose a representation learning model of community-augmented heterogeneous information network embedding (CAHE), which integrates community structure to preserve community structure information of various types of nodes. In detail, we assume that the different types of nodes exist in the different community structures for a heterogeneous information network, and define community embedding as a set of Mixture Gaussian models. Then, we unify the community embedding of all types of nodes to improve the capacity of network representation. A large of experimental results of several kinds of homogeneous and heterogeneous information network representation learning on two public datasets suggest that the proposed CAHE is superior to other several state-of-art methods.

The accurate land cover change detection is critical to improve the landscape dynamics analysis and mitigate desertification problems efficiently. Desertification detection is a challenging problem because of the high degree of similarity between some desertification cases and like-desertification phenomena, such as deforestation. This article provides an effective approach to detect deserted regions based on Landsat imagery and variational autoencoder (VAE). The VAE model, as a deep learning-based model, has gained special attention in features extraction and modeling due to its distribution-free assumptions and superior nonlinear approximation. Here, a VAE approach is applied to spectral signatures for detecting pixels affected by the land cover change. The considered features are extracted from multitemporal images and include multispectral information, and no prior image segmentation is required. The proposed method was evaluated on the publicly available remote sensing data using multitemporal Landsat optical images taken from the freely available Landsat program. The arid region around Biskra in Algeria is selected as a study area since it is well-known that desertification phenomena strongly influence this region. The VAE model was evaluated and compared with restricted Boltzmann machines, deep learning model, and binary clustering algorithms, including Agglomerative, BIRCH, expected maximization, k-mean clustering algorithms, and one-class support vector machine. The comparative results showed that the VAE consistently outperformed the other models for detecting changes to the land cover, mainly deserted regions. This study also showed that VAE outperformed the state-of-the-art algorithms.

The recent applications of fully convolutional networks (FCNs) have shown to improve the semantic segmentation of very high resolution (VHR) remote-sensing images because of the excellent feature representation and end-to-end pixel labeling capabilities. While many FCN-based methods concatenate features from multilevel encoding stages to refine the coarse labeling results, the semantic gap between features of different levels and the selection of representative features are often overlooked, leading to the generation of redundant information and unexpected classification results. In this article, we propose an attention-guided label refinement network (ALRNet) for improved semantic labeling of VHR images. ALRNet follows the paradigm of the encoder-decoder architecture, which progressively refines the coarse labeling maps of different scales by using the channelwise attention mechanism. A novel attention-guided feature fusion module based on the squeeze-and-excitation module is designed to fuse higher level and lower level features. In this way, the semantic gaps among features of different levels are declined, and the category discrimination of each pixel in the lower level features is strengthened, which is helpful for subsequent label refinement. ALRNet is tested on three public datasets, including two ISRPS 2-D labeling datasets and the Wuhan University aerial building dataset. Results demonstrated that ALRNet had shown promising segmentation performance in comparison with state-of-the-art deep learning networks. The source code of ALRNet is made publicly available for further studies.

In order to improve the global search ability of K-means algorithm and the clustering effect, a K-means method based on the approximate backbone and the shuffled frog leaping algorithm was proposed. Firstly, the classic iterative formula of the K-means algorithm is replaced by the classic shuffled frog leaping algorithm to obtain better clustering results. Secondly, the K-means algorithm based on the approximate backbone and the shuffled frog leaping algorithm is used for the obtained clustering results. Instead of searching for cluster centers, the cluster division is directly modified. Finally, the experimental results on the UCI dataset show that, the running time of the improved clustering algorithm is shorter than that based on the shuffled frog leaping algorithm only, and clustering results obtained by using the improved clustering algorithm are better than those of other algorithms. In addition, the paper uses the improved clustering algorithm to preprocess medical fundus images to optimize the effect of vascular cutting.

Cross-project defect prediction (CPDP) aims to build a prediction model on existing source projects and predict the labels of target project. The data distribution difference between different projects makes CPDP very challenging. Besides, most existing CPDP methods usually require sufficient and labeled data. However, acquiring lots of labeled data for a new project is difficult while obtaining the unlabeled data is relatively easy. A desirable approach is building a prediction model on unlabeled data and labeled data. CPDP in this scenario is called cross-project semi-supervised defect prediction (CSDP). Recently, generative adversarial networks have achieved impressive results with these strong ability of learning data distribution and discriminative representation. For effectively learning the discriminative features of data from different projects, we propose a Discriminative Adversarial Feature Learning (DAFL) approach for CSDP. DAFL consists of feature transformer and project discriminator, which compete with each other. A feature transformer tries to generate feature representation, which learns the discriminant information and preserves intrinsic structure inferred from both labeled and unlabeled data. A project discriminator tries to discriminate source and target instances on the generated representation. Experiments on 16 projects show that DAFL performs significantly better than baselines.

Wind power is one of the most potential energies and the major available renewable energy sources. Precisely predicting wind power production is essential for the management and the integration of wind power in a smart grid. The goal of this study is to predict wind power production with sufficient accuracy based on various factors using ensemble learning-based methods that consider the time-dependent nature of the wind power measurements. Essentially, the ensemble learning methods combine multiple learners to obtain an enhanced prediction performance in comparison to conventional standalone learners. In addition, they reduce the overall prediction error and have the capacity to merge various models. At first, this paper investigates the prediction capability of the well-known ensemble approaches Boosted Trees, Random Forest, and Generalized Random Forest for wind power prediction. We compared the prediction performance of these ensemble models to two frequently used prediction methods: Gaussian process regression, and Support Vector Regression. Experimental measurements recorded every ten minutes from actual wind turbines located in France and Turkey are used to test the prediction efficiency of the studied models. Experimental results have shown that the ensemble methods can predict wind power production with high accuracy compared to the standalone models. Furthermore, the findings clearly reveal that the lagged variables contribute significantly to the ensemble models, and permits constructing more parsimonious models.

Accurately detecting Parkinson's disease (PD) at an early stage is certainly indispensable for slowing down its progress and providing patients the possibility of accessing to disease-modifying therapy. Towards this end, the premotor stage in PD should be carefully monitored. An innovative deep-learning technique is introduced to early uncover whether an individual is affected with PD or not based on premotor features. Specifically, to uncover PD at an early stage, several indicators have been considered in this study, including Rapid Eye Movement and olfactory loss, Cerebrospinal fluid data, and dopaminergic imaging markers. A comparison between the proposed deep learning model and twelve machine learning and ensemble learning methods based on relatively small data including 183 healthy individuals and 401 early PD patients shows the superior detection performance of the designed model, which achieves the highest accuracy, 96.45% on average. Besides detecting the PD, we also provide the feature importance on the PD detection process based on the Boosting method.

Digital watermarking is an effective method of vector map copyright protection. However, the topology and geometric features are potentially influenced by the disturbances of vertices caused by watermark embedding, thus reducing the availability of watermarked vector maps. In this research, we propose a post-correction method for two-dimensional vector map watermarking, by which the topology and geometric features of the input data can be preserved, while using only conventional watermarking techniques. In the proposed method, the Maximum Perturbation Regions (MPR) of vertices and direction angle constraints methods of adjacent lines are adopted to undergo multi-azimuth checks of watermarked vertices, which may cause changes of the geometric features and topology. After that, the coordinate adjustment method which is based on the homonymous vertices topology association and the MPR are combined for topology and geometric feature correction. For watermark embedding, two classic frequency domain watermarking techniques, Discrete Fourier Transformation (DFT) and Discrete Wavelet Transformation (DWT), phase based and low-frequency coefficient based respectively, are also adopted. The scheme was conducted on a building vector map and a road vector map. The experimental results show that the proposed method can ensure the topology consistency and geometric feature similarity of vector maps before and after embedding watermarks. Moreover, this method has little interference with the maps, which improves the usability of the watermarked vector map.

In order to effectively solve the problem of interlaced overlap in the fundus image lesions, large and small blood vessels packed densely and severely affected by light, and to achieve multi-label classification of fundus images. In this paper, a single population leapfrog optimization convolutional neural network algorithm (SFCNN) is proposed to detect and classify various fundus lesions. The algorithm uses the efficient search ability of the shuffled frog leaping algorithm to optimize the weight initialization and back propagation of the convolutional neural network. In order to deal with the problem of fundus image classification in the big data environment, the novel grouping optimization strategy is presented to effectively combine Spark platform and SFCNN algorithm to achieve large-scale fundus image classification and detection of multiple lesions. The experiment of the detection of fundus image lesions shows that the accuracy rate of SFCNN is better improved in both single lesion detection and overall detection, compared with other algorithms.

Emotion plays a significant role in perceiving external events or situations in daily life. Due to ease of use and relative accuracy, Electroencephalography (EEG)-based emotion recognition has become a hot topic in the affective computing field. However, scalp EEG is a mixed-signal and cannot directly indicate the exact information about active cortex sources of different emotions. In this paper, we analyze the significant differences of active source regions and frequency bands for pairs of emotions-based reconstructed EEG sources using sLORETA, and 26 Brodmann areas are selected as the regions of interest (ROI). And then, six kinds of time- and frequency-domain features from significant active regions and frequency bands are extracted to classify different emotions using support vector machines. Furthermore, we compare the classification performances of emotion features extracted from active source regions and EEG sensors. We have demonstrated that the features from selected source regions can improve the classification accuracy by extensive experiments on the DEAP and TYUT 2.0 EEG-based datasets.

In the space-ground integrated networks, many tasks come from different access networks, such as cellular network, Wireless Fidelity (WIFI), Satellite network, Unmanned Aerial Vehicle (UAV) and so on. And these tasks have different priorities in general. Therefore, how to couple these tasks in the core network and design a low computational complexity and high-efficiency task processing strategy is a big challenge. In this paper, the concept of virtual queues is introduced to improve the performance of the space-ground integrated networks. The task queue can be split into several virtual queues according to their priorities and the type of access networks. To evaluate the performance of virtual queues, the system performance index is defined. Finally, the system performance of the space-ground integrated networks is analyzed in detail. The result can be applied into the design of the space-ground integrated networks.

The operating temperature of the magnetic coupler in wireless power transfer (WPT) systems for electric vehicles (EVs) determines the system reliability and service life. This paper studies the modeling and analysis of temperature characteristics of a general magnetic coupler. Firstly, a thermal circuit model is established to show the heat transfer of the magnetic coupler in the air. Secondly, the heating mechanism of the coil, core and aluminum (Al) shield plate is studied. The thermal power of each component is calculated, and the temperature distribution is qualitatively given based on the proposed thermal circuit model. Then, the temperature distribution of each component is simulated, and the results are consistent with the theoretical analysis. Finally, a 6.6 kW WPT prototype is set up, and the temperature of each component is measured after 30 minutes of operation. The temperature of the coil, 34.6 °C, is the highest among that of the coil, core, Al plate and coil base. The agreement between experimental results and the theoretical simulation results shows that the thermal field simulation can accurately predict the temperature of the magnetic coupler by reasonably setting the thermal power addition method.

This paper focuses on the observer-based $H_\infty $ control issue for large-scale systems under denial-of-service attacks. Due to the security vulnerability of communication networks, adversaries have the capability to hamper the system normal operation by blocking the signal exchange in both sensor-to-observer channels and the observer-to-observer channels. Under this scenario, a decentralized proportional-derivative-like controller is employed to realize the predetermined $H_\infty $ performance while considering the improvement of dynamic response of closed-loop systems. By using the Lyapunov stability theory, a sufficient condition dependent on the attack occurring probability is established to realize the desired control performance. Furthermore, in light of the established condition, the desired controller and observer gains are formalized by resorting to the orthogonal decomposition of control matrix. Finally, the effectiveness and practicability of the control scheme are illustrated by resorting to the numerical simulation of a four area power system.

The accurate forecast of wastewater treatment plant (WWTP) key features can comprehend and predict the plant behavior to support process design and controls, improve system reliability, reduce operational costs, and endorse optimization of overall performances. Deep learning technologies as proven data-driven soft-sensors should be developed for WWTP applications to tackle the process of non-linearity and the dynamic nature of environmental data. This study adopts deep learning-based models as soft-sensors to forecast WWTP key features, such as influent flow, influent temperature, influent biochemical oxygen demand (BOD), effluent chloride, effluent BOD, and power consumption. We constructed six deep learning models derived from long short-term memory (LSTM) and gated recurrent unit (GRU), namely traditional LSTM and GRU, the exponentially smoothed LSTM, and the adaptive version of LSTM and smoothed LSTM. The employment of a smoothed LSTM technique is expected to reduce the outlier effect and to improve forecasting accuracy. Meanwhile, the usage of adaptive deep models will enhance the capabilities of the LSTM to quickly and accurately follow the trend of future data. We compared the performance of these models with Bi-directional LSTM (BiLSTM) and the seasonal decomposition using local regression. The historical records from a coastal municipal WWTP in Saudi Arabia are used to verify the investigated models' effectiveness. The proposed models provide promising forecasting results but require no assumptions on the data distributions. In terms of efficiency, GRU based models converge faster than LSTM based models. In terms of accuracy, the LSTM soft-sensor shows overall the optimal result for all key features followed by the exponentially-smoothed GRU and LSTM. By contrast, the adaptive models achieved the lowest forecasting performance compared to the other models. These findings will benefit practitioners to achieve data-driven WWTP management.

Aimed to automate the segmentation of organs at risk (OARs) in head and neck (H&N) cancer radiotherapy, we develop a novel Prior Attention enhanced convolutional neural Network (PANet) based Stepwise Refinement Segmentation Framework (SRSF) on full-size computed tomography (CT) images. The SRSF is built with a multiscale segmentation concept, in which OARs are segmented from coarse to fine. PANet is a pyramidal architecture with elements of inception block and prior attention. In this study, the developed PANet based SRSF is applied for OARs segmentation in H&N radiotherapy. 139 CT series and manually delineated contours of twenty-two OARs by experienced oncologists are collected from 139 H&N patients for training and evaluating the proposed PANet based SRSF. The mean testing Dice similarity coefficients (DSC) on 39 CT series range from 76.1± 8.3% (left middle ear) to 91.9± 1.4% (right mandible) for large volume OARs(mean volume >1cc) while the corresponding ranges are 63.4± 12.3%(chiasm) to 81.0± 14.1% (right lens) for small and challenging OARs(mean volume ≤1cc). Furthermore, the proposed method also achieved superior segmentations over reference methods on the MICCAI 2015 H&N dataset with mean DSC of 95.6± 0.7%, 81.3± 4.0%, 77.6± 4.5%, 77.5± 4.6%, and 69.2± 7.6%, on the mandible, left submandibular, left and right optical nerve, and chiasm, respectively. The accurate segmentation of OARs is obtained on both the self-collected testing data and public testing dataset, which implies that the proposed method can be used as a practicable and efficient tool for automated OARs contouring in the H&N cancer radiotherapy.

With the development of smart cities, lots of mobile cooperative sensing based nodes have emerged. However, due to the open nature of wireless transmission, attackers in the networks can use some intelligent radio devices to deteriorate the secure transmission, which imposes a severe issue of information leakage. In this paper, we consider the transmitter has some computational tasks to be computed, under the environments of intelligent attacker. Due to the limited computational capability, the sender needs to offload some tasks to the receiver. To address this problem, we propose a power allocation algorithm based on combining the technology of reinforcement learning and game theory, in order to achieve an optimal secure data rate and meanwhile reduce the whole task latency of the transmission and computation with Q learning and Nash equilibrium. Then, the Nash equilibrium and its existence conditions are derived and proven mathematically. Finally, we perform some simulations under Matlab platform, and the results show that the proposed algorithm can effectively improve the secrecy data rate and reduce the whole system latency.

For the flange end of the internal combustion engine crankshaft, the position of threaded holes and pinhole relative to the journal reference plane affects the dynamic balance and thermal efficiency of the engine, and therefore, this parameter measurement has a positive significance in the crankshaft manufacture. This paper proposes a system for quickly measuring the relative position of the flange-end holes, which uses a pneumatic linear variable differential transformer to form measuring benches for getting the position of the journal reference plane, and the high precision coordinates of the flange-end holes are obtained by multi-camera. The coordinates of the flange-end holes are merged into the global coordinate system of the journal reference plane, and the experimental result shows that the position uncertainty is better than 0.1 mm. This method has the advantage of convenient loading and unloading, fast measuring speed, and high accuracy and is easily implemented in the flexible manufacturing line for a full inspection of the semi-finished crankshaft.

A fully electronic time reversal mirror (TRM) system, which can make signal time reversal transformation, is proposed in this paper. The high-order asymmetric all-pass equalizer (APE) is proposed to provide a prescribed group delay for the TRM system. Finally, two APEs are designed to be used in a demonstrated TRM, which has a time duration of 10 ns and a bandwidth of 1 GHz.

To ensure the integrity of cloud storage, it is necessary for data owners to launch data integrity auditing (DIA) periodically. To fit more application scenarios, the research direction is shifted to design identity-based DIA (ID-DIA) protocols without expensive public key infrastructure, which can resist the quantum computing attacks simultaneously. However, the existing ID-DIA protocols are usually based on heavy anti-quantum computing cryptographies, which might severely limit their practical deployments. To address this problem, we explore three ways to design RLWE-based ID-DIA (ID-DIA-RLWE) protocols. The first is to improve the existing LWE-based ID-DIA protocols. The second is to enhance the existing RLWE-based DIA protocols. The third is to instantiate the proposed semantic ID-DIA protocol. Among these three proposed protocols, the third one is the most efficient due to its simplest construction. We also propose a new security model of ID-DIA protocols, including correctness assurance, security guarantee, and privacy protection. Then, we provide theoretical analysis of all proposed ID-DIA protocols under the given security model. Finally, we conduct extensive performance evaluation and comparison, which further validate that the third protocol has superiority in the perspectives of computation, communication, and storage costs.

Urban intelligence is an emerging concept which guides a series of infrastructure developments in modern smart cities. Human-computer interaction (HCI) is the interface between residents and the smart cities, it plays a key role in bridging the gap in applicating information technologies in modern cities. Hand gestures have been widely acknowledged as a promising HCI method, recognition human hand gestures using surface electromyogram (sEMG) is an important research topic in the application of sEMG. However, state-of-the-art signal processing technologies are not robust in feature extraction and pattern recognition with sEMG signals, several technical problems are still yet to be solved. For example, how to maintain the availability of myoelectric control in intermittent use, since pattern recognition qualities are greatly affected by time variability, but it is unavoidable during daily use. How to ensure the reliability and effectiveness of myoelectric control system also important in developing a good human-machine interface. In this paper, linear discriminant analysis (LDA) and extreme learning machine (ELM) are implemented in hand gesture recognition system, which is able to reduce the redundant information in sEMG signals and improve recognition efficiency and accuracy. The characteristic map slope (CMS) is extracted by using the feature re-extraction method because CMS can strengthen the relationship of features cross time domain and enhance the feasibility of cross-time identification. This study is focusing on optimizing the time differences in sEMG pattern recognition, the experimental results are beneficial to reducing the time differences in gesture recognition based on sEMG. The recognition framework proposed in this paper can enhance the generalization ability of HCI in the long term use and it also simplifies the data collection stage before training the device ready for daily use, which is of great significance to improve the time generalization performance of an HCI system.

Discovering the behavior pattern of urban high-risk driving is of great significance for national economy development and social stability. With increasing traffic accidents and crimes on current urban roads, the government endeavors to reduce the traffic violation rate. Exploring the behavior pattern of urban high-risk driving is an effective approach to prevent traffic accidents. In this paper, we explore the urban-scale high-risk driving behavior pattern in China. In order to achieve this goal, we analyze the death records of traffics among more than 300 cities throughout the country. In particular, we first build a city network, in which a node represents the properties of the traffic violation for the discovery of the urban-scale high-risk driving behavior pattern. Six urban-scale high-risk driving behavior patterns about overspeeding, nonlicense, drunk, and violation of the traffic rules are discovered from the traffic violation city network data. The research result leads to three key conclusions. First, urban networks based on death records information show strong spatial dependence and hierarchical characteristics and are largely coupled with the space distribution of major cities in China, reflecting the spatial relationship and core-periphery structure of high-risk driving behavior on the urban-scale. Second, to a large extent, there is a frequent occurrence of death record information around or in major domestic cities, economic belts, and important coastal ports, such as Beijing, Tianjin, Shanghai, the Yangtze River Delta Economic Belt, and the Pearl River Delta Economic Belt. This phenomenon demonstrates the attractiveness of large cities and regions as a domestic economic and political center. Third, the geographical location of the city has an impact on the pattern of high-risk driving behavior. For example, Tangshan city, the largest production base of mineral resources in China, possessing several transportation routes and heavy vehicle flows on the road, shows an increasing probability of violation of the traffic law.

This paper investigates the two problems of generalized chaotic synchronization. In one case, the Chen system is treated as the drive system and the controlled Rucklidge system as the response system. In the other case, we choose the Rucklidge system as the drive system and the controlled Chen system as the response system. For each case, a scalar control input and a state transformation are proposed by using the differential geometric approach when a simple condition holds. Under the scalar control input and the state transformation, the trajectory of the response system can track the trajectory of the drive system no matter what initial values of the two systems. The two generalized synchronizations realized in this paper reveal the Rucklidge system and the Chen system are similar in some sense although their system equations are quite different. Finally, several numerical simulations are studied to demonstrate the control schemes given in this paper.

For D2D-based vehicular communication networks, how to effectively and quickly allocate frequency resources is a big challenge. In this paper, a resource allocation scheme based on immune algorithm for D2D-based vehicular communication networks is proposed. Firstly, the D2D-based vehicular communication system model is described. Then the resource allocation issue in D2D-based vehicular communication networks is formulated and analyzed. Next, an efficient resource allocation scheme based on immune algorithm is presented. Finally, the analysis and simulation results validate the high efficiency of the proposed scheme compared with other algorithms. This result can provide the guidance for the deployment of VUE communication system.

Falls are an important healthcare problem for vulnerable persons like seniors. Response to potential emergencies can be fastened timely detection and classification of falls. This paper addresses the detection of human falls using relevant pixel-based features reflecting variations in body shape. Specifically, the human body is divided into five partitions that correspond to five partial occupancy areas. For each frame, area ratios are calculated and used as input data for fall detection and classification. First, the detection of falls is addressed from a statistical point of view as an anomaly detection problem. Towards this end, an integrated approach merging a detection step with a classification step is proposed for enabling efficient human fall detection in a home environment. In this regard, an effective fall detection approach using generalized likelihood ratio (GLR) scheme is designed. However, a GLR scheme cannot discriminate between true falls and like-fall events, such as lying down. To mitigate this limitation, the support vector machine algorithm has been successfully applied on features of the detected fall to recognize the type of fall. Tests on two publicly available datasets show the effectiveness of the proposed approach to appropriately detecting and identifying falls. Compared with the neural network, k -nearest neighbor, decision tree and naïve Bayes procedures, the two steps approach achieved better detection performance.

To operate wastewater treatment plants (WWTPs) with optimized efficiency, influent conditions (ICs) as initial states of inflow fed to WWTPs were monitored to identify potential anomalies that would trigger adverse events or system crash. To employ voluminous measurements for data-driven decisions, the non-linear, non-Gaussian, non-stationary, auto-correlated, cross-correlated, hetero-skedastic, case-specific nature of multivariate environmental datasets must be considered. This research proposed kernel machine learning models, the kernel principal components analysis based one-class support vector machine (KPCA-OCSVM) with various kernels, to learn anomaly-free training set then classify the testing set. A seven-years multivariate ICs time series was introduced with exploratory analysis performed to reveal temporal behaviors and statistical properties. KPCA with polynomial kernels sufficiently output representative features, based on which OCSVM with Gaussian kernels sensitively and specifically identified anomalies in ICs that were previously omitted by WWTP operators. The proposed kernel algorithms surpassed previous linear PCA-based K-nearest-neighbors models, and improved outcomes with limited increase in computation cost. Without requiring linear, Gaussian, stationary, independent, and homo-skedastic qualities from data, the proposed flexible environmental data science approach could be transferred, rebuilt, and tuned conveniently for ICs from different WWTPs.

Aggregation is a vital behavior when performing complex tasks in most of the swarm systems, such as swarm robotics systems. In this paper, three new aggregation methods, namely the distance-angular, the distance-cosine, and the distance-Minkowski k-nearest neighbor (k-NN) have been introduced. These aggregation methods are mainly built on well-known metrics: the cosine, angular, and Minkowski distance functions, which are used here to compute distances among robots' neighbors. Relying on these methods, each robot identifies its k-nearest neighborhood set that will interact with. Then, in order to achieve the aggregation, the interactions sensing capabilities among the set members are modeled using a virtual viscoelastic mesh. Analysis of the results obtained from the ARGoS simulator shows a significant improvement in the swarm aggregation performance compared to the conventional distance-weighted k-NN aggregation method. Also, the aggregation performance of the methods is reported to be robust to partially faulty robots and accurate under noisy sensors.

Studying event-related potentials (ERPs) is considered as an effective method for investigating cerebral mechanisms of processing emotional speech. It has been shown that the amplitudes of ERP components in the cognitive processing of emotional speech are modulated by acoustic characteristics, such as valence and arousal. However, whether the duration of emotional speech stimuli impacts emotion-related cognitive processing remains unclear. To better understand the effect of emotional speech stimulus duration on emotion-related cognitive processing, we explored whether emotional speech ERPs were influenced by the duration of stimuli presented. Specifically, this paper focused on the ERP investigation of different durations (short: 0.50-1.00 s; medium: 1.50-2.00 s; and long: 2.50-3.00 s) of Chinese emotional speech stimuli. Chinese is a typical tonal language, and the stimuli were excerpted from radio plays in order to make emotions more obvious and easier to distinguish. We investigated the three different stages of the emotional speech processing: sensory processing, salience detection, and cognition. During the experiment, participants passively listened to emotional utterances matched for semantics and prosody with four emotions (sadness, anger, happiness, and surprise). Our results showed significant differences in the amplitudes of ERP components for different emotions during short-duration emotional speech stimuli. These findings suggest that shorter duration emotional speech stimuli may be more effective for separating the ERP components representing different emotions (N100, P200, and N300).

Nowadays, blockchain has become one of the most cutting-edge technologies, which has been widely concerned and researched. However, the quantum computing attack seriously threatens the security of blockchain, and related research is still less. Targeting at this issue, in this paper, we present the definition of post-quantum blockchain (PQB) and propose a secure cryptocurrency scheme based on PQB, which can resist quantum computing attacks. First, we propose a signature scheme based on lattice problem. We use lattice basis delegation algorithm to generate secret keys with selecting a random value, and sign message by preimage sampling algorithm. In addition, we design the first-signature and last-signature in our scheme, which are defined as double-signature. It is used to reduce the correlation between the message and the signature. Second, by combining the proposed signature scheme with blockchain, we construct the PQB and propose this cryptocurrency scheme. Its security can be reduced to the lattice short integer solution (SIS) problem. At last, through our analysis, the proposed cryptocurrency scheme is able to resist the quantum computing attack and its signature satisfies correctness and one-more unforgeability under the lattice SIS assumption. Furthermore, compared with previous signature schemes, the sizes of signature and secret keys are relatively shorter than that of others, which can decrease the computational complexity. These make our cryptocurrency scheme more secure and efficient.