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The positioning systems’ high accuracy and reliability are crucial enablers for various future applications, including autonomous shipping worldwide. It is especially challenging for the Arctic region due to the lower number of visible satellites, severe ionospheric disturbances, scintillation effects, and higher delays than in the non-Arctic and non-Antarctic regions. In regions up North, conventional satellite positioning systems are generally proposed to be utilized, together with other situational awareness systems, to achieve the necessary level of accuracy. This paper provides a detailed review of the current state-of-the-art, satellite-based positioning systems’ availability and performance and reports high-level positioning requirements for the oncoming applications. In particular, the comparative study between three Global Navigation Satellite System (GNSS) constellations is executed to determine whether they are suitable for autonomous vessel navigation in the Arctics’ complex environment as the two most significant drivers for a reevaluation of the related satellite constellations. This work analyzes the ongoing research executed in different (inter-) national projects focused on Galileo, Global Positioning System (GPS), and GLObal NAvigation Satellite System (GLONASS). Based on the literature review and the simulation campaign, we conclude that all the convectional constellations achieve an accuracy of fewer than three meters in the analyzed Arctic scenarios. It is postulated that other complementary positioning methods should be utilized to improve accuracy beyond this limit. Finally, the study emphasizes existing challenges in the Arctic region regarding the localization and telecommunication capabilities and provides future research directions. publishedVersion

This paper presents a lane change decision algorithm for predictive decision-making for an autonomous vehicle using a Recurrent Neural Network (RNN) with a Bidirectional Long Short-Term Memory (Bi-LSTM) cell. The proposed decision-making algorithm was trained and validated by driving data collected by vision, laser scanners, and chassis sensors of autonomous vehicles. The input features for the Bi-LSTM based RNN consist of the clearance and relative velocity with the surrounding target vehicles, lane measurements, and the velocity of the autonomous vehicle. The output features are configured to generate the probability of three maneuvers, left lane change, right lane change, and lane-keeping. The Bi-LSTM based RNN is configured to decide in advance two seconds before lane changes by using two seconds of observation. The collected 20,108 datasets were accumulated in global coordinates. After processing and resampling the collected datasets, 1,120, 320, and 160 datasets were generated to train, validate, and test the Bi-LSTM based RNN. The proposed algorithm was evaluated by a case study and a driving data-based prediction accuracy analysis. The results of the predictive lane change decision by the proposed algorithm have been shown to be more accurate and similar to a driver than previous approaches.

To deal with the problem where each instance is associated with multiple labels, a lot of multi-label learning algorithms have been developed in recent years. Some approaches have been proposed to select label-specific features to utilize discriminate features for multi-label classification. Although label correlation has been considered in learning label-specific features, the critical correlation among instances was less taken into account. In this paper, we proposed a new approach called multi-label learning with label-specific features using correlation information (LSF-CI) to learn label-specific features for each label with the consideration of both correlation information in label space and correlation information in feature space. In the LSF-CI, the instance correlation in feature space is computed by a probabilistic neighborhood graph model, and label correlation in label space is computed by cosine similarity. For multi-label data, the LSF-CI has the capability to select Label-specific features for each label as well as classify an unseen instance into a set of relevant labels. To validate the effectiveness of LSF-CI, we conducted comprehensive experiments on eight multi-label datasets. The experimental results demonstrate that the LSF-CI is capable of selecting compact label-specific features, and achieving a competitive performance in comparison with the performances of the existing multi-label learning approaches.

This paper presents a novel single-phase to single-phase multiconverter topology that can be applied in multiple areas. The proposed multiconverter is designed with only two soft power semiconductor switches (e.g. MOSFET or IGBT), four power diodes and a center-tapped transformer which makes it more compact in size, decrease the gate driving complexity, reduce the total equipment costs and enhance the energy conversion efficiency with minimized losses. Furthermore, the utilization of the transformer in the proposed converter mitigates the multiple AC source requirement problems and provides galvanic isolation which increases the reliability of the converter. Moreover, the presented multiconverter is applicable in various areas including electric traction as a speed controller, induction heating, AC and DC variable power supplies, etc. which signify the competence of this converter in energy conversion appliances. However, a comparative analysis of the offered converter with the existing AC-AC converters is also introduced in this paper with respect to the number of components, equipment costs, gate driving complexity, and application areas. In order to evaluate the performance of the proposed multiconverter, the simulation-based results carried out in MATLAB/Simulink are presented and analyzed in this paper with proper descriptions. Finally, a scaled-down prototype is developed in the laboratory to validate the simulation results and the feasibility of the proposed multiconverter.

In pantographs, current collector strips transmit the electrical energy they receive from the catenary to the locomotive and provide the necessary power for the locomotive's movement. In order for the current collector strips to transmit electricity to the locomotive in a healthy way, their surface must be smooth. Wear on the surface of the current collector strips reduces conductivity and can create arcs, endangering the health and safety of the pantograph and catenary system. In this paper, a Convolutional Neural Network (CNN) architecture is developed to detect wear on the current collector strips. Images obtained from pantographs used on railways were created with a clean and improved data set using Hough Transform and Power Law Transform. The created dataset contains 909 pantograph images. This dataset was trained and tested with both the developed CNN architecture and classic deep learning architectures (ResNet50, VGG16). The experimental results show that the developed CNN architecture's training results and test results are more successful than classical architectures.

To ensure a long-time stable operation of the rolling bearing, it is important to accurately assess their working performance, especially the incipient degradation based on the massive service process data. As a new and effective tool, deep learning model is applied widely in the field of fault diagnosis but limited to rare labeled data. In this paper, a bearing performance assessment method based on signal component tracking is proposed to realize the bearing degradation detection. More general features are obtained by local convolution operation to represent the local characteristics in the spectrum or time–frequency distribution of vibration signal, which follows the forward features mapping process of the convolutional neural network (CNN). Then, a novel quantification criterion based on the comparison of those local features is used to provide the selection strategy of optimal fault components. The proposed method takes into account the abnormal information in degradation monitoring and utilizes it to achieve bearing incipient fault diagnosis. The experimental results prove that the features extracted by the proposed method possess high recognition efficiency when being used in incipient failure detection and diagnosis.

Covariance analysis describing function technique is a conventional method to solve the performance analysis of the nonlinear missile guidance system. Aiming at the faultiness of covariance analysis describing function technique and its improved method, this paper proposes a new method to solve the mean and covariance transfer issue by using the high-degree spherical-radial cubature rule. In terms of simulation models, the nonlinear missile guidance system contains the saturation link to limit the lateral acceleration command and the initial heading error is introduced. In addition, the zero-mean target evasive maneuver with exponentially-decaying autocorrelation function is chosen as the input of the missile guidance system. Furthermore, compared with the existing methods, the proposed method has higher computational efficiency, accuracy and better applicability in high-dimensional nonlinear systems by theoretical derivation and simulation results from the mean and covariance values of relative lateral separation. Finally, the presented analysis method can be used as a general method for the design and statistical analysis of multidimensional nonlinear systems.

Nonlinear Model-based Predictive Control (NMPC) is a relevant research area having applications in the industrial sector. Traditionally, in this technique, gradient descent algorithms have been used to solve the related optimization problem. More recently, bio-inspired meta-heuristics have also been applied to this problem. However, only a few works have been devoted to testing solvers that use parameter control with self-adaptive traits, which allows mitigating the problem of offline parameter tuning in bio-inspired approaches. In this paper, we propose the novel Adaptive Modified Grey Wolf Optimization (AMGWO) and the Adaptive Moth-Flame Optimization (AMFO), for solving Nonlinear Model-based Predictive Control (NMPC) problems. To achieve this, a mechanism for individual leaders weighting and a crossover operator are introduced in AMGWO, and a simple self-adaptive parameter technique is applied in both meta-heuristics. The improved solvers are tested to perform the swing-up of a single inverted pendulum and attitude control of a satellite, which are nonlinear problems relevant for assessing control performance. Nonparametric statistical tests are applied to compare the improved meta-heuristics optimization outcomes with other five meta-heuristics, which shows that the self-adaptive parameter technique can significantly improve the performance when applied as an NMPC solver, as the AMFO and AMGWO statistically outperform or performs as well as all algorithms compared in both the pendulum and satellite control, respectively. This is important as improving the optimizer efficiency will lead to more accurate control and enable rapid hardware implementation.

Intelligent Transportation Systems (ITS) research and applications benefit from accurate short-term traffic state forecasting. To improve the forecasting accuracy, this paper proposes a deep learning based multitask learning Gated Recurrent Units (MTL-GRU) with residual mappings. To enhance the performance of the MTL-GRU, feature engineering is introduced to select the most informative features for the forecasting. Then, based on real-world datasets, numerical results show that the MTL-GRU can well estimate traffic flow and speed simultaneously, and performs better than other counterparts. Experiments also show that the deep learning based MTL-GRU model can overpower the bottleneck caused by enlarging training datasets and continue to gain benefits. The results suggest the proposed MTL-GRU model with residual mappings is promising to forecast short-term traffic state.

This article analyzes the serum electrolyte disturbances of patients through deep learning algorithms. Among the 104 patients with electrolyte disturbances, 6 cases of serum potassium, sodium, chloride, calcium, phosphorus, and magnesium electrolyte disturbances have occurred, the proportion of occurrence The order is sodium > chlorine > calcium > potassium > phosphorus > magnesium. This paper proposes a deep learning algorithm for serum electrolyte disorder, and analyzes and implements the functions at various levels according to the characteristics of the Hadoop framework. The system includes electronic medical records shared storage, distributed realization of definite learning algorithms, classification and recognition of myocardial ischemia by deep learning, and Web system assisting doctor diagnosis, which lays the foundation for the construction of serum electrolyte disorder scientific research data center. In order to explore this relationship, without artificially extracting features, a deep learning model of convolution and long-term and short-term memory circulation neural network cascade was proposed to determine the positive or negative myocardial ischemia by classifying serum disorders. Conduct clinical experiments, including patients with suspected coronary heart disease and coronary angiography as the research object, taking coronary angiography results as the detection standard. Experimental results show that the model has an accuracy of 89.0% for detecting myocardial ischemia, a sensitivity of 91.7%, and a specificity of 81.5%. A linear combination model of CNN and LSTM is proposed to classify and recognize serum electrolyte disorders. Determine the learning theory to dynamically model the ST-T segment in the ECG to obtain serum electrolyte disturbance, which more vividly shows the changes in electrical information under myocardial ischemia. In order to reveal the relationship between serum electrolyte disturbance and myocardial ischemia, this paper builds a neural network model to learn and train serum electrolyte disturbance data to realize the classification of positive or negative myocardial ischemia. Tests on serum electrolyte disturbance data collected in clinical experiments show that this model can better achieve early detection of myocardial ischemia.

Robot-assisted cooperative rehabilitation training has shown superiority in helping the individuals with motion impairment problems to regain their motor functions. This paper presents the development and evaluation of a new cooperative training control scheme for an end-effector-type rehabilitation robot to provide upper extremity rehabilitation training with desired compliance and intensity. Firstly, the overall mechanical structure and real-time control system of rehabilitation robot are introduced. Secondly, an integral fuzzy sliding mode impedance control strategy combined with time-delay estimation (IFSMIC-TDE) is proposed to induce the active participation of patients and suppress impedance error. The IFSMIC-TDE approach is free from nonlinear robot dynamics, and it is designed to be robust to the external uncertainties and inherent chattering characteristics. After that, the closed-loop system stability with IFSMIC-TDE is proved based on the Lyapunov stability theory. Finally, experimental investigations are conducted to illustrate the effectiveness of the proposed rehabilitation robot and control algorithm. The comparison results indicate that the proposed IFSMIC-TDE can achieve better control performance and less impedance error. Besides, the interaction compliance and training intensity can be qualitatively adjusted via appropriate impedance parameters.

In traditional wireless sensor networks (WSN), data is transmitted to base stations through multiple-hop. However, multi-hop transmission will cause problems such as energy holes, uneven energy consumption, and unreliable data transmission. Aiming at the defects of traditional networks, this paper proposes a scheme for collaborative data collection using multiple mobile nodes (MN) as sink nodes. In this scenario, an important issue is how to reasonably plan the data collection path of each mobile node under a series of constraints such as energy. This paper studies routing strategy and path planning. Firstly, a dynamic clustering algorithm is used to cluster the randomly arranged sensor nodes, and then a sensor node with higher energy is manually arranged at the virtual cluster center generated by the clustering algorithm as a cluster head node to establish a data collection cluster. Then the monitoring area is divided into several parts according to the number of MN, so that the MN traverses the cluster head nodes in the respective monitoring areas for data collection. At the same time, in order to enable the MN to complete data collection within the energy limit and improve the path balance, a path-based path equalization algorithm (PEABR) is proposed to adjust the path of the MN, further reduce and equalize the path length of the MN to satisfy the constraint conditions, and optimize the path planning scheme. Finally, simulation was carried out by using Matlab simulator, and the simulation experiments which were performed in a laboratory environment verified the feasibility and effectiveness of the algorithm.

Emerging deep learning (DL) techniques have greatly improved pedestrian reidentification (PRI) performance. However, the existing DL-based PRI methods cannot learn robust feature representations owing to the single view of query images and the limited number of extractable features. Inspired by generative adversarial networks (GANs), this paper proposes a novel PRI method based on a pedestrian multiview GAN (PmGAN) and a classification recognition network (CRN). The PmGAN consists of three generators and one multiclass discriminator. The three generators produce pedestrian images from the front, side and back, while the multiclass discriminator determines whether the input image is a real image or a generated image. In addition to expanding the existing pedestrian datasets, the PmGAN can generate pedestrian images from front, side and back views based on a given query image and thereby increase the feature semantic space of the query image. To verify the performance of our method, the PmGAN was compared with mainstream pedestrian image generation models, and then the proposed method was contrasted with mainstream PRI methods. The results show that the proposed PmGAN greatly improved the performance of mainstream PRI methods. For example, the combination of the PmGAN and Pyramidal increased the mean average precision (mAP) on three common datasets by 1.2% on average. The research findings provide new insights into the application of multiview generation in PRI tasks.

Accurate remaining useful life (RUL) prediction under the noisy environment is a big challenge for the health management of modern industrial systems since the extraction of the accurate data structure from heavily corrupted data is difficult. In recent years, the kernel adaptive filter (KAF) has been widely adopted to solve the robust regression problem due to its low-complexity and high-approximation capability and robustness while the applications in battery RUL prediction are still few and far between. Thus, this paper is concerned with long-term RUL prediction using the KAF method. At first, concretely speaking, a robust KAF algorithm is derived based on the double-Gaussian-mixture (DGM) cost function, which is used to learn the capacity degradation mechanism from contaminated capacity data and so as to build the long-term prediction model. Second, a robust unscented Kalman filter (UKF) algorithm employing the DGM-based cost function is developed, which is then combined with the KAF-based prediction model to realize a more accurate and reliable prediction. Under the hybrid prognostic framework, the proposed UKF algorithm is applied to filter the noisy observations. When the observation data are inaccessible, the predicted data from the off-line trained KAF-based prediction model are adopted as the approximated value of the real observations for the UKF algorithm to optimize the prediction results and to provide the uncertainty representation. The experimental results reveal that the proposed method has great robustness when the measurements contain noise and large outliers, which makes it possible to get satisfactory prediction performance without preprocessing the data manually.

Hyperspectral transmission imaging may provide a means for rapid screening of breast tumors, but tissue has a strong nature of scattering, thus causing a great difficulty in identifying heterogeneity. In this paper, a combination of frame accumulation and deep learning was proposed to detect heterogeneity, and we designed the simulation experiment of collecting phantom images. On the basis of frame accumulation preprocessing, the heterogeneous detection is performed on multispectral images by using faster regions with convolutional neural networks (R-CNN) features and a single shot multibox detector (SSD), two typical detection frameworks of deep learning. The results show that the mean average precision (mAP) of faster R-CNN and SSD reach 90.8% and 95.1%, respectively, when three classes (including background) are detected with the help of the dataset provided in this paper, and the mAP of two frameworks both reach 99.9% when two classes (including background) are detected. The detection efficiency of the SSD is higher than faster, SSD’s detection speed can reach 50 fps, and the detection accuracy of the images after frame accumulation preprocessing is higher than that without frame accumulation processing. In summary, we validate the possibility of employing faster R-CNN and SSD to detect heterogeneity in multispectral images based on frame accumulation that improves image grayscale resolution, and it has a certain degree of reference significance for the application of deep learning in multispectral image detection.

This paper presents an effective constrained multiple model particle filtering (CMMPF) for bearings-only maneuvering target tracking. In the proposed algorithm, the process of target tracking is factorized into two sub-problems: 1) motion model estimation and model-conditioned state filtering according to the Rao–Blackwellised theorem and 2) the target dynamic system is modeled by multiple switching dynamic models in a jump Markov system framework. To estimate the model set, a modified sequential importance resampling method is used to draw the model particles, which can be restricted into the feasible area coincide with the constrained bound. To the model-conditioned state nonlinear filtering, a truncated prior probability density function is constructed by utilizing the latest observations and auxiliary variables (target spatio–temporal features), which can guarantee the diversity and accuracy of the sampled particles. The tracking performance is compared and analyzed with other conventional filters in two scenarios: 1) uniform and time-invariant sampling scenario and 2) non-uniform and sparse sampling scenario. A conservative Cramer–Rao lower bound is also introduced and compared with the root mean square error performance of the suboptimal filters. Simulation results confirm the superiority of CMMPF algorithm over the other existing ones in comparison with respect to accuracy, efficiency, and robustness for the bearings-only target tracking system, especially for the aperiodic and sparse sampling environment.

Today, agriculture industry has a significant impact in global greenhouse gas emissions. A large amount of pollutants come from diesel internal combustion engines, widely used in agricultural machinery. Since mechanization in agriculture is fundamental to achieve a proper food production for a growing human population, changes are needed in common agriculture engineering thinking in order to develop new farming machinery that could outperform conventional ones in terms of environmental impact, as well as performance, productivity and safety. Electrification is a feasible solution. A comprehensive review about agricultural machinery electrification is reported in this paper, with a particular focus on hybrid electric tractors and their implements. The introduction of electric drives in farming tractors is discussed in detail by looking at the main findings in literature and considering state-of-the-art technology. Proposals and prototypes from manufacturers are covered too, as well as economic assessments and future trends.

In recent years, the quantity of digital data being generated has increased considerably and is overwhelming the storage capacity. To overcome this problem, acquiring more and larger data storage is the simplest solution. But this solution is rather costly and may produce poisonous electronic garbage. A new fast and memory-efficient algorithm for learning and classifying these data without increasing the space and time complexities more than those of current learning and classifying algorithms is desirable. Although many one-pass online or incremental learning algorithms based on hyperellipsoidal functions for streaming data without retaining any learned data in fixed storage have been successfully developed for training streaming data, achieving high accuracy of any testing dataset is unstable and uncontrollable, depending on the experimental datasets. This paper proposes an improvement to these one-pass and fixed-storage learning algorithms so that the high accuracy of testing data can be significantly improved and stabilized, regardless of the experimental datasets. The concept is based on animal recoil behavior, which occurs when an animal moves away suddenly from something it dislikes. The behavior is mathematically modeled in forms of shrinking and shifting the hyperellipsoidal function during the training period to improve testing accuracy. The experimental results on 15 datasets improved the accuracy up to 8.16 % and also provided the highest or near-highest accuracy results in 10 datasets when compared to other algorithms.

This paper addresses the problem of target-directed exploration (TDE) in initially unknown and large-scale indoor environments. In such scenarios, the inference on an unknown space can improve the search performance under the assumption that the context of a particular space (i.e., the functional category of the space) is correlated with the existence of a target. The space inference is promising in that there is a strong statistical correlation between the semantic categories of indoor spaces and their adjacency because the spaces are designed to reflect universal human preferences. In this point of view, we propose a novel TDE scheme leveraging the semantic-spatial relations of an indoor floorplan dataset. Whereas existing works dealing with the data-driven space inferences consider only the one-to-one relation statistics of the spaces or utilize heuristic counting-based matching algorithms without building a trainable latent model, we propose the pattern cognitive Multivariate Bernoulli Distribution-based Graphical Space Inference Model (MBD-GSIM). MBD-GSIM efficiently captures the core contexts of the discrete semantic-spatial relations to predict an unknown space by using the latent Multivariate Bernoulli Distribution model. We also suggest utilizing the MBD-GSIM in a cost-utility based frontier exploration scheme for TDE problems. The proposed scheme is constructed in the Robot Operating System (ROS); its efficiency is investigated in the Gazebo simulation environment.

In this paper, a new image segmentation method is proposed by combining the FCM clustering algorithm with a rough set theory. First, the attribute value table is constructed based on the segmentation results of FCM under different clustering numbers, and the image is divided into several small regions based on the indistinguishable relationship of attributes. Then, the weight values of each attribute are obtained by value reduction and used as the basis to calculate the difference between regions and then the similarity evaluation of each region is realized through the equivalence relationship defined by the difference degree. Finally, the final equivalence relation defined by similarity is used to merge regions and complete image segmentation. This method is validated in the segmentation of artificially generated images, brain CT images, and MRI images. The experimental results show that compared with the FCM method, the proposed method can reduce the error rate and achieve better segmentation results for the fuzzy boundary region. And, the experimental results also prove that the algorithm has strong anti-noise ability.

Intelligent Vehicle (IV) is expected to revolutionize the way vehicles operating in the next few decades. The most commonly used method to test the functionality of the IV is to use the IV testbed for operable and economic reasons. However, the traditional testbed only has the ability to test the longitudinal movement and is not capable of studying the lateral movement of the IV, which extremely restricts the developments of the IV testing technologies (e.g., IV lane-keeping and lane-changing testing). To this end, this paper presents a novel steering-following system (SFS) for the intelligent vehicle-in-the-loop (VIL) testbed. The SFS is able to obtain the real-time steering angle differences between the front wheels and the drum roller by two-pair laser distance sensors equipped on the testbed. Then the value and changing rate of the obtained real-time steering angle difference are set as the inputs of a fuzzy sliding mode control (FSMC) algorithm. The outputs of the FSMC algorithm are used to control the operation of the servo motors. With that, the testbed accurately tracks the steering maneuver of the IV at any time. A simulation-based experiment and a VIL-based experiment show that the designed SFS with the FSMC algorithm is able to provide accurate and stable tracking results for the IV steering maneuvers under different scenarios.

Team composition is one of the most important and challenging directions in the recommendation problem. Compared with a single person, the advantage of a team is mainly reflected in the synergy of team members’ complementary collaboration. To build a high-efficiency team, how to choose the team members has become a tricky problem. However, there is a lack of quantitative algorithms and validation methods for team member selection. In this paper, we put forward three indicators to measure a team’s ability and formulate the selection of football team members as a multi-objective optimization problem. Subsequently, an evolutionary player selection algorithm based on the genetic algorithm is proposed to solve the team composition problem. We verify the effectiveness of the team member recommendation algorithm via data analysis, football game simulation under different budget constraints and provide comparisons with existing methods.

Face age progression/regression has garnered substantial active research interest due to its tremendous impact on a wide-range of practical applications like searching for missing individuals with photos of childhood, entertainment, and so on. Most existing face aging models have proven to be successful and effective in learning the transformation between age groups with the aid of paired samples, i.e., face images of the same person at different ages. Considering the expensive cost of collecting paired datasets, Conditional Adversarial Autoencoder (CAAE) is designed for face aging task without paired samples and first achieves face age progression and regression in a holistic framework. However, only rough wrinkles are generated because of the insufficient discriminative and generative ability. To tackle this problem, in this paper, we develop a novel generative model based on CAAE, dubbed CAAE++, which defeats the previous CAAE mainly for two enhancements: 1) an auxiliary classifier is added on top of the discriminator, which allows a single discriminator not only distinguishes real images from synthetics but also classifies them into the target age group; and 2) a pre-trained deep face recognition model and a pre-trained age estimation model are exploited to preserve identity and age similarity, respectively. We train CAAE++ on UTKFace dataset and test on FGNET dataset. Experimental results demonstrate the efficacy of our proposed method in terms of fidelity.

In this paper, an ultra-miniaturized implantable antenna is proposed for biomedical applications, which operates in frequency of the industrial, scientific, and medical bands of 2.4 GHz. The miniaturization of the proposed antenna is obtained by using a meander line as radiating patch, introducing a $1.2~\Omega $ chip resistor, and two pairs of a rectangle slot are etched on the ground plane to expand the antenna impedance bandwidth. The antenna has an ultra-compact size with a width and length of 2.5 mm and a thickness of 0.64 mm (the total dimension of $2.5 {\mathrm {mm}} \times 2.5 {\mathrm {mm}} \times 1.28 {\mathrm {mm}}^{3}$ ). To validate the antenna structure, the optimal design is fabricated and experimentally measured. A low loss, flexible, and biocompatible PCB material, Taconic CER-10 ( $\varepsilon _{r} = 10.2, \sigma = 0.0 035$ ) is adopted as both the substrate and superstrate. The experiment results imply that the proposed antenna produces a good impedance matching at 2.4 GHz with a bandwidth of 16.6% and a maximum peak gain of −18.3 dBi. In addition, to further prove the performance of the designed biomedical implantable antenna, the effects of a coaxial cable and the simulations in the environment of the human tissue models (head, arm, and leg) are established. The antenna provides a low specific absorption rate (SAR) with the value compliance the IEEE standard safety guidelines. To the best of our knowledge, the proposed antenna is the smallest dimension (in width and length) with high performance compared to previously reported works. Thus, the antenna is a potential candidate and suitable for the biomedical applications.

Estimation methods are generalized in this paper by the idea of “scalar-vector-matrix”. A generalized contraction mapping (GCM) framework is proposed for searching the optimal linear biased estimation. First, based on the latent model and the mean square error criterion, four different biased estimation methods are analyzed. They are the improved principal component estimation (PCE), the improved principal component estimation (IPCE), the ridge estimation (RE), and the generalized ridge estimation (GRE). A suboptimal ridge parameter for the RE is given. Four estimation performance theorems for the four methods are obtained using the traditional contraction mapping (CM) framework. The theoretical results can ease the difficulty of choosing methods for application. Second, we generalize the CM framework into the generalized contraction mapping (GCM) framework, and the optimal linear biased estimation method based GCM is given theoretically by the geometric tools of rotation, contraction, and reflection. Therefore, the GCM framework further improves the estimation performance. Finally, a numerical experiment is designed to validate the correctness of the theorems in the paper.

The explosive growth of video data traffic brings a great pressure on the future 5G networks. Deploying caches at the radio access network and evolved packet core in the 5G network can effectively relieve this pressure by reducing duplicate content retransmission. In this paper, we propose a new collaborative cache scheme for the future 5G wireless network. The proposed scheme aims to improve the data availability and access rate locally easing collaborative local resources of the 5G wireless hotspot network. We investigate the impact of different constraints on the collaborative cache scheme, including limited storage capacity, placement of content, content popularity, and distribution of content. By considering these constraints, we effectively improve the overall performance of the 5G wireless network in terms of transmission delay, local availability of contents, and efficient utilization of caches within the network hotspot. Our proposed scheme provides an efficient utilization of the hotspot cache storage by using Zipf distribution and knapsack, and it is also able to entertain the user request partially available in the hotspot. Simulation results reveal that compared with conventional collaborative cache schemes, the proposed scheme for the 5G wireless network can significantly reduce transmission delay and increase hit-ratio within the hotspot.

Modulated wideband converter (MWC) is a multi-branch sub-Nyquist sampling structure for processing the spare wideband signals. By mixing with a periodic pseudo-random bit sequence (PRBS), each branch of MWC compresses the information of the input signal into a narrow baseband which can be sampled at low speed. Based on the several sampling sequences, the input signals can be reconstructed by compressed sensing (CS) optimization algorithms. However, the classic MWC (C-MWC) still consumes a lot of hardware resources because of its complex structure. Especially when the spectrum is blind, the total sampling rate is required to be four times the Landau rate for joint sparse reconstruction. To solve this problem, this paper proposes alternate MWC (A-MWC). A-MWC replaces the periodic PRBSes with two sets of alternating PRBSes and uses a trigger to synchronize the PRBSes and the sample sequences. Based on a reasonable assumption that the input signals are the wide-sense stationary (WSS) signals which have the same power spectral density (PSD) in each time slice, this paper reduces the requirement the sampling rate for blind joint sparse reconstruction to twice Landau rate successfully. Both simulation and experiment prove that A-MWC reduces the number of branches required for blind reconstruction without reducing the quality of the reconstructed signal.

High-performance single-ended wideband and balanced bandpass filters loaded with stepped-impedance stubs are proposed in this paper. For the proposed wideband filter and the differential mode of the proposed balanced filter, two transmission zeros near the passband are realized by using simple stepped-impedance stubs, which enhances the passband selectivity of these new filters. The analytical and accurate design equations for transmission poles and zeros of these single-ended and balanced filters are provided based on the scattering-parameters theory. The single-ended bandpass filter, employing a T-shaped structure to produce a wide passband, is achieved with 3-dB fractional bandwidth of 79.33%, 19-dB passband return loss, and 35-dB harmonic suppression ( $1.56f_{\mathrm { {0}}}$ – $2.51f_{\mathrm { {0}}}$ , $f_{\mathrm { {0}}}$ is the center frequency of the wide passband). The 3-dB differential-mode fractional bandwidth of 33% with 22-dB passband return loss and the 30-dB harmonic suppression ( $1.29f_{\mathrm { {0}}}$ – $2.67f_{\mathrm { {0}}})$ are obtained in the proposed balanced bandpass filter. In addition, by inserting open-circuit stubs into the proposed balanced bandpass filter, over 25-dB common-mode suppression is obtained from 0 to $2.67f_{\mathrm { {0}}}$ . Good agreement is observed between the simulation and measurement results.

Aiming at the limitation that traditional methods for combat intention recognition of aerial targets are difficult to effectively capture the essential characteristics of intelligence information, we design a novel deep learning method, Panoramic Convolutional Long Short-Term Memory networks (PCLSTM), to improve the recognition ability. First, based on the characteristics of aerial target intelligence information, a panoramic convolutional layer is designed to extract the loosely coupled characteristics of intelligence information, and a time series pooling layer is designed to reduce the scale of neural network parameters on a large scale. Then, the temporal feature extraction capability of the LSTM layer and the depth feature mining capability of the traditional deep learning layer are combined to construct the PCLSTM neural network. Subsequently, the recognition performance of PCLSTM is analyzed by simulation experiments compared with standard deep net, convolutional neural network and LSTM network as benchmark models. Finally, PCLSTM was used to carry out simulation tests on different truncated data sets of original intelligence information, to analyze the optimal length of truncated data for different combat intention recognition. And then a reasonable aerial target combat intention recognition method is designed. The simulation results show that the method presented in this paper has theoretical significance and reference value for command decision-making.

Deep learning-based techniques are the state of the art in road traffic prediction or forecasting. Several deep neural networks have been proposed to predict the traffic but they have not been evaluated under common datasets. Current studies analyze their capacity to predict road traffic in general but do not focus on their capacity to predict the formation of congestions. This is critical for avoiding congestions or mitigate their negative impact. This paper progresses the current state of the art by presenting a comprehensive comparison of the state-of-the-art deep neural networks for road traffic prediction. The comparison is conducted using the same real traffic datasets, and under normal and congested traffic conditions. The evaluation includes new deep neural networks and error recurrent models. Our study first demonstrates that accurately predicting the traffic overall does not imply that a deep neural network can accurately predict the traffic when congestions are being formed. This reinforces the idea that prediction techniques must also be evaluated under congestion conditions. Our analysis also shows that exploiting the spatiotemporal evolution of the traffic (and not just the temporal one) provides better prediction accuracy overall and in particular under congestion conditions. The study also demonstrates that error recurrent models outperform deep neural networks that do not utilize an error feedback both under normal and congested traffic conditions. In particular, our study shows that the error recurrent model eRCNN is the deep learning technique that achieves to date the best traffic prediction accuracy. It is also important emphasizing that error recurrent models achieve better prediction accuracy with shallower neural networks and therefore lower computational cost.

Android malware is increasing in spread and complexity. Advanced obfuscation, emulation detection, delayed payload activation or dynamic code loading are some of the techniques employed by the current malware to hinder the use of reverse engineering techniques and anti-malware tools. This growing complexity is particularly noticeable in the evolution of different strands of the same malware family. Over the years, these families mature to become more effective by incorporating new and enhanced techniques. In this paper, we focus on a particular Android ransomware family named Jisut, and perform a thorough technical analysis. We also provide a detailed overall perspective, which will hopefully help to create new tools and techniques to tackle more effectively the threat posed by ransomware.

This paper presents a method to design an integrated chassis controller with four-wheel independent steering (4WIS) under the constraint on front slip angles for electric vehicles (EVs) adopting in-wheel motor (IWM) driving system. To improve lateral stability and maneuverability of a vehicle, direct yaw moment control strategy is adopted. A control allocation method is adopted to distribute control yaw moment into tire forces, generated by 4WIS. If corrective steering angles of 4WIS are added to front steering angles generated by a driver, it can deteriorate control performance because the lateral tire force of front wheels easily saturated and it causes loss of required yaw moment needed to stabilize a vehicle. To cope with the problem, it is necessary to impose constraints on front slip angles. To compensate the loss of control yaw moment caused by the constraint on front slip angles, a constrained control allocation method is presented. Simulation on driving simulation tool, CarSim®, shows that the proposed integrated chassis controller is capable of maintaining lateral stability and maneuverability without performance deterioration under the constraint on the front slip angles.

The concept of the sharing economy has attracted wide attention due to its huge impact on transforming traditional industries. Online car-hailing, combining the sharing economy and ICT technologies, shapes a new landscape, which can greatly shorten the traveler's waiting time and reduce the empty-run rate of cars (such as Uber and DiDi). However, a few comprehensive studies have been conducted on the sustainable development of online car-hailing considering both the user experience and the operational cost. To address this issue, this paper systematically studies the influencing factors, their relations, and their impacts on online car-hailing in an empirical way. First, an index system in four key aspects, namely service, price, safety, and traveling time, is established to evaluate the user experience. Second, the Bayesian network theory is employed to model the complexity of each factor and the extent of its influence on the online car-hailing system with expert scores. The two most important influence paths affecting the passengers' choices of online car-hailing are determined. Third, we further construct an investment allocation model with the aim of minimizing the economic cost of the online car-hailing system while maintaining the system performance, considering the limiting factors, such as the complexity and cost. Finally, we perform a simulation experiment, which generates some practical suggestions for improving the online car-hailing system.

Recently, the leaky diffusion least-mean-square (DLMS) algorithm has obtained much attention because of its good performance for high input eigenvalue spread and low signal-to-noise ratio (SNR). However, the leaky DLMS algorithm may suffer from performance deterioration in the sparse system. To overcome this drawback, the leaky zero attracting DLMS (LZA-DLMS) algorithm is developed in this paper, which adds an l1-norm penalty to the cost function to exploit the property of sparse system. The leaky reweighted zero attracting DLMS (LRZA-DLMS) algorithm is also put forward, which can improve the estimation performance in the presence of time-varying sparsity. Instead of using the l1-norm penalty, in the reweighted version, a log-sum function is employed as the substitution. Based on the weight error variance relation and several common assumptions, we analyze the transient behavior of our findings and determine the stability bound of the step-size. Moreover, we implement the steady state theoretical analysis for the proposed algorithms. Simulations in the context of distributed network system identification illustrate that the proposed schemes outperform various existing algorithms and validate the accuracy of the theoretical results.

Currently, population growth is global and tends to concentrate in large cities, which increases the demand for illuminating public spaces for safety, visual orientation, aesthetic considerations, and quality of life. The undesirable side effects are increase in energy consumption and light pollution. The current tools used for designing public lighting systems are not suitable for optimizing multiple objectives in addition to energy savings, and these solutions could provide for a more sustainable environment. The application of evolutionary optimization techniques seems to be growing rapidly because of the nonlinearity of the model behavior and the nonproprietary nature of the algorithms, which are considered as black box systems. This paper develops a data model for these types of optimizers, analyzing the ability of different arti cial neural network (ANN) architectures to simulate a simple public lighting design by measuring the performance with respect to the tness function, training speed, and goodness of t with a dataset generated with different conditions. The architectures selected in this paper are those with multilayer perceptrons (MLPs) with different hidden layer con gurations using different numbers of neurons in each layer, which have been analyzed to determine the con guration that best ts the purpose of this work. The data for training the ANNs were generated with a recognized open-software platform, DIALux. The experiments were repeated and analyzed to determine the variance of the results obtained. In this way, it was possible to identify the most appropriate number of iterations required. The results show that better precision is obtained when using the Levenberg Marquardt training algorithm, especially when the ANN architecture has fewer neurons in the hidden layer. post-print 1234 KB

The presence of missing data is problematic in most quantitative research studies. Water distribution systems (WDSs) are not immune to this problem. In fact, missing data is an inherent feature of a WDS. There are various techniques and methods to address missing data ranging from simply deleting the data to using complex algorithms to impute missing data. This paper reviews the different imputation options available from traditional methods (such as deletion and single imputation) to more modern and advanced methods (such as multiple imputation, model-based procedures, and machine learning techniques). The concept, application, and qualitative advantages and disadvantages of these methods are discussed. In addition, a novel approach for selecting an applicable technique is presented. The approach is a “top-down bottom-up”two-prong approach for the selection of a data analysis and missing data technique. The bottom-up approach facilitates the top-down selection of a suitable technique by analyzing the data and narrowing down the selection options. As a use case, this paper also reviews techniques that are used to impute missing data in WDSs.

Relational fuzzy clustering (RFC) algorithms prove very useful in Web user session clustering because Web user sessions may contain fuzzy, conflicting and imprecise information. Though RFC algorithms are very sensitive to cluster initialization and works only if the numbers of clusters are specified in advance. However, at all times, the prior initialization of a number of clusters is not feasible due to the dynamically evolving nature of user sessions. Therefore, estimating the number of clusters and initializing suitable cluster prototype are a significant performance bottleneck in this method. In this paper, the discounted fuzzy relational clustering (DFRC) algorithm is proposed to address the major constraint of RFC. The DFRC algorithm identifies Web user session clusters from Web server access logs, without initializing the number of clusters and prototypes of initial clusters. The DFRC algorithm works in two stages. In the first stage, DFRC automatically identifies the number of potential clusters based on the successively discounted potential density function value of each relational data and their respective centres. In the second stage, DFRC assigns fuzzy membership values to each data point and forms fuzzy clusters from the relational matrix. The DFRC algorithm is applied on an augmented session dissimilarity matrix obtained from a publicly accessed NASA Web server log data. The experimental results are evaluated using different fuzzy validity measures. The extensive experiments are performed to test the effect of various parameters, including accept/reject ratio and neighbourhood radius on the performance of DFRC algorithm. The results were also compared with fuzzy relational clustering algorithm using cluster quality measures. It is observed that the quality of generated clusters using DFRC is superior as compared with that of RFC.

To cope with severe environmental problems, the government has implemented strict emission control policies, and promoted the use of technology such as shore power (SP) in the form of subsidies. The SP providers of the shipping lines like the ports provide better SP services to shipping lines by improving its reliability, such as safety and standardization. This paper examines a two-echelon maritime supply chain consisting of a port and a shipping line under government green subsidy and explores the subsidy mechanism and its impact. The optimal government subsidy intensity and subsidy reduction point are confirmed using game theory. The system dynamics (SD) method is used to analyze the influence and evolution of practical problems such as government subsidy efficiency, information asymmetry, and inconsistent decision-making periods under multiple games utilizing the optimal response function. The paper shows that both shipper SP preference and decision period affect the SP reliability. When the shipper's preference is high, the actual shipper's subsidies and government subsidies are inefficient. Information asymmetry borne by ports and shipping line causes considerable fluctuations in the game and does not affect the actual subsidies received by shippers. Also, shipping line downstream of the supply chain are more affected by it. This paper offers insight for the government to formulate subsidy policies in the maritime supply chain.

The problem of $l_{2} - l_\infty $ controller design for a class of discrete-time descriptor systems is addressed in this paper. In other words, the controller is designed to ensure the admissibility and the prescribed $l_{2} - l_\infty $ performance for the descriptor control system. The $l_{2} - l_\infty $ performance analysis and the feedback controller design of the descriptor control system are studied by the matrix inequality technique. Novel design conditions of the feedback controller for the descriptor systems are introduced in terms of linear matrix inequality (LMI) representations. An RC circuit is applied to explain the effectiveness of the proposed $l_{2} - l_\infty $ control design method.

The cooperative triple-phase-shifted modulation (CTPS) for the isolated dual-active bridge (DAB) DC-DC converter has the advantages of zero dual-DC side flow back currents and best current characteristics. However, the mathematical model and corresponding closed-loop control configuration of the DAB converter driven by CTPS are not presented so far. In order to solve this problem, in this paper, the average state space model of the DAB converter driven by CTPS is established for the first time. It is proven that the DAB converter driven by CTPS is a zero-order system and it is not necessary to construct an inner inductor current closed-loop control loop in the entire closed-loop control configuration of DAB converter. Furthermore, the average power model is derived. Two control strategies based on the resulted average power model when the DAB converter is connected to DC microgrid or a pure resistive load are proposed to realize the precise control of the transfer power or the output voltage, respectively. The detailed experimental results verify the correctness of the established mathematical model and the closed-loop control strategies.

The two-way relaying (TWR) technique has been confirmed to achieve higher spectral efficiency and average sum rate compared with the one-way relaying (OWR) technique in ultra-dense next generation networks with limited spectal resources. In this paper, an enhanced secrecy cooperative TWR scheme for cooperative non-orthogonal multiple access (NOMA) networks against untrusted relaying is highlighted. Specifically, with the application of NOMA principle, a base station (Bs) communicates with two trusted users, i.e., namely a near and a far users, where the communication with the far user takes place only via an untrusted relay (UR) employing both TWR and analog network coding (ANC) mechanisms. To minimize information leakage at the UR, the far user transmits its uplink signal simultaneously with Bs’s downlink signal to confuse the eavesdropping capability of the UR by increasing inter-user interference (IUI). To investigate the benefits of the proposed scheme, asymptotic lower bound expressions of the ergodic secrecy sum rate (ESSR) of uplink/downlink rates and their scaling law are derived to characterize the secrecy performance. The system parameters have been carefully studied to maitain the desired ESSR performance where we obtain an optimal value for the uplink power sharing coefficient with an arbitrary known value of the downlink one. Analytical and simulation results show that the proposed scheme can achieve scaling gain of $\boldsymbol {\frac {1}{8}\ln {\rho }}$ of positive ESSR over the untrusted OWR scheme with adaptive uplink/downlink jamming and a significant gain over the other conventional NOMA uplink/downlink schemes in two time slots of communication.

With the expansion of the use of frequency response analysis (FRA) as a reliable tool for fault detection in transformers, more capabilities of this method are discovered every day. So that today the number of transformer faults that can be identified by FRA method has also increased. One of the most critical steps in fault detection with FRA is to distinguish faults and classify them in different classes. In this paper, well-known intelligent classifiers (probabilistic neural network, decision tree, support vector machine, and k-nearest neighbors) are used to classify transformer faults. For this purpose, the necessary measurements are performed on the model transformers under the healthy condition and under different fault conditions (axial displacement, radial deformation, disc space variation, short-circuits, and core deformation). Then, by dividing the frequency ranges of the measured transfer functions of the transformer, a new feature based on numerical and statistical indices for training and validation of classifiers is proposed. After completing the training process, the performance of the classifiers is evaluated and compared by applying the data obtained from real transformers.

Pattern synthesis is a significant research focus in smart antennas due to its extensive use in several radar and communication systems. To improve the optimization performance of pattern synthesis of uniform and sparse linear antenna array, this paper presents an optimization method for solving the antenna array synthesis problem using the Mayfly Algorithm (MA). MA is a new heuristic algorithm inspired by the flight behavior as well as the mating process of mayflies, it has a unique velocity update system with great convergence. In this work, the MA was applied to linear antenna arrays (LAA) for optimal pattern synthesis in the following ways: by optimizing the antenna current amplitudes while maintaining uniform spacing and by optimizing the antenna positions while assuming a uniform excitation. Constraints of inter-element spacing and aperture length are imposed in the synthesis of sparse LAA. Sidelobe level (SLL) suppression with the placement of nulls in the specified directions is also implemented. The results gotten from this novel algorithm are validated by benchmarking with results obtained using other intelligent algorithms. In the synthesis of uniform 20-element LAA with nulls, MA achieved an SLL of −31.27 dB and the deepest null of −101.60 dB. Also, for sparse 20-element LAA, an SLL of −18.85 dB was attained alongside the deepest null of −87.37 dB. MA obtained an SLL of −35.73 dB and −23.68 dB for the synthesis of uniform and sparse 32-element LAA respectively. Finally, electromagnetism simulations are conducted using ANSYS Electromagnetics (HFSS) software, to evaluate the performance of MA for the beam pattern optimizations, taking into consideration the mutual coupling effects. The results prove that optimization of LAA using MA provides considerable enhancements in peak SLL suppression, null control, and convergence rate with respect to the uniform array and the synthesis obtained from other existing optimization techniques.

Many-objective optimization problems (MaOPs) present a huge challenge to the traditional Pareto-based multi-objective algorithms because the increase of the objectives results in the low-efficiency of the Pareto dominance in distinguishing the relationships between the solutions during the environmental selection. To enhance the selection pressure, in this paper, through redefining each objective function by a non-linear transformation, we first propose a new dominance method called NLAD-dominance, in which a dynamic parameter adjusting scheme is designed to dynamically adjust parameter α according to different numbers of objectives and different evolutionary states. As a result, NLAD-dominance can provide proper selection pressure for different kinds of MaOPs in different stages of evolution. Then, based on NLAD-dominance, we design a new fitness estimation strategy which takes both convergence and diversity into account, and adaptively balances them by a parameterless penalty rule. Thus, it can well evaluate the quality of each solution. At last, we conduct the experiments and compare the proposed algorithm with five state-of-the-art algorithms on 80 test instances of 16 benchmark problems with up to 20 objectives. The experimental results indicate that the proposed algorithm is highly competitive in terms of both convergence enhancement and diversity maintenance.

In open and dynamic multiagent systems (MASs), agents often need to rely on resources or services provided by other agents to accomplish their goals. During this process, agents are exposed to the risk of being exploited by others. These risks, if not mitigated, can cause serious breakdowns in the operation of MASs and threaten their long-term wellbeing. To protect agents from the uncertainty in the behavior of their interaction partners, the age-old mechanism of trust between human beings is re-contexted into MASs. The basic idea is to let agents self-police the MAS by rating each other on the basis of their observed behavior and basing future interaction decisions on such information. Over the past decade, a large number of trust management models were proposed. However, there is a lack of research effort in several key areas, which are critical to the success of trust management in MASs where human beings and agents coexist. The purpose of this paper is to give an overview of existing research in trust management in MASs. We analyze existing trust models from a game theoretic perspective to highlight the special implications of including human beings in an MAS, and propose a possible research agenda to advance the state of the art in this field.

In this paper, we propose an efficient distortion-based privacy-preserving metering scheme that protects an individual customer's privacy and provides the complete power consumption distribution curve of a multitude of customers without privacy invasion. In the proposed scheme, a random noise is purposely introduced to distort customers' power consumption data at the smart meter so that data recovery becomes infeasible. Using the power consumption data and prior knowledge about added random noise, we develop an efficient algorithm for power consumption distribution reconstruction needed for power demand analysis and prediction. As a complete solution, our scheme also supports a privacy-preserving billing service. Using experimental results from real world single household power consumption data set and synthesized data of a large number of households, we demonstrate that the proposed scheme is robust against known attacks. Since it does not demand new facilities on existing smart grids, the proposed scheme offers a practical solution.

This paper provides the design and implementation of an L1-optimal control of a quadrotor unmanned aerial vehicle (UAV). The quadrotor UAV is an underactuated rigid body with four propellers that generate forces along the rotor axes. These four forces are used to achieve asymptotic tracking of four outputs, namely the position of the center of mass of the UAV and the heading. With perfect knowledge of plant parameters and no measurement noise, the magnitudes of the errors are shown to exponentially converge to zero. In the case of parametric uncertainty and measurement noise, the controller yields an exponential decrease of the magnitude of the errors in an L1-optimal sense. In other words, the controller is designed so that it minimizes the L∞-gain of the plant with respect to disturbances. The performance of the controller is evaluated in experiments and compared with that of a related robust nonlinear controller in the literature. The experimental data shows that the proposed controller rejects persistent disturbances, which is quantified by a very small magnitude of the mean error.