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Cyber-physical-social systems (CPSS) have recently gained attention from researchers due to their combination of cyber, physical, and social spaces. Modeling and Analysis of Real-Time and Embedded systems (MARTE) is a Unified Modeling Language (UML) extension profile that supports the specification, design, and verification of Real-Time Embedded Systems (RTES). While MARTE Statecharts can assist in describing CPS, it does not model the uncertainty within a CPSS environment. To enhance the accuracy of CPSS analysis, we propose the stohMCharts (stochastic hybrid MARTE statecharts) modelling framework as an extension of MARTE statecharts for modelling and analyzing stochastic hybrid systems. stohMCharts can model CPSS in a unified manner. Additionally, based on the mapping rules and algorithms, we have developed a tool to convert models built in stohMChart language into Networks Stochastic Hybrid Automata (NSHA) which can be verified by statistical model checker UPPAAL-SMC. We demonstrate the efficiency and accuracy of the framework by applying it to one autonomous driving scenarios.

Childlessness or infertility among couples has become a global health concern. Due to the rise in infertility, couples are looking for medical supports to attain reproduction. This paper deals with diagnosing infertility among men and the major factor in diagnosing infertility among men is the Sperm Morphology Analysis (SMA). In this manuscript, we explore establishing deep learning frameworks to automate the classification problem in the fertilization of sperm cells. We investigate the performance of multiple state-of-the-art deep neural networks on the MHSMA dataset. The experimental results demonstrate that the deep learning-based framework outperforms human experts on sperm classification in terms of accuracy, throughput and reliability. We further analyse the sperm cell data by visualizing the feature activations of the deep learning models, providing a new perspective to understand the data. Finally, a comprehensive analysis is also demonstrated on the experimental results obtained and attributing them to pertinent reasons.

In many applications of wireless power transfer (WPT) systems, there are situations where the transmitter needs to supply power to receivers of different frequencies or powers. Therefore, it is very important to improve its compatibility and adaptability. In this paper, a dual-frequency compatible WPT system with a single transmitter and multiple receivers is proposed. The transmitter current contains two main frequency components obtained by current modulation and resonance compensation. The receivers of different frequencies pick up the corresponding components by frequency selective resonance. Therefore, decoupled transfer and independent control can be achieved. In this paper, a dual-frequency compensation topology is constructed and an adjustment method for receiver power decoupling control is proposed. Through theoretical and experimental analysis, the efficacy characteristics of the system have been evaluated, and its dual-frequency compatibility and power decoupling control capability have been verified. Compared with the single-frequency multiple-receiver system, this system can avoid the problems of mutual coupling: induced output power and system efficiency reduction, transmitter detuning, reactive power increase and inverter output capacity reduction.

Towards the massive amount of data generated in our daily work and life, embedded systems, with economical but powerful storage and computing resources, are inevitably becoming the most suitable platform for the Edge Computing for the Internet of Things. However, embedded system servers may also threaten individuals by storing individuals’ private data for years. This paper proposes a Resilient Tag-based Privacy Protection (RTPP) scheme for embedded systems. Specifically, to protect the privacy against the hackers and other non-users, we employ a pseudo-random number encryption technique with the chaos-based principle so that the third party cannot easily steal the private data and reduce the risk of personal privacy leakage. To protect the individuals’ interests, we propose a new approach to controlling the life cycle table of data to enable individuals themselves the flexibility to control the life cycle of private data. Unlike existing data lifetime management methods, the RTPP can support the retrieval of tags in the data life cycle table to control the corresponding privacy while automatically adding or removing tags. Our system automatically adjusted the survival period of private data in the life cycle table through the change of leaf weights, controlled the charge movement on the surface of flash memory, and finally achieved the resilient adjustment process of the life cycle of private data in the embedded system. The security proof and performance evaluation show that the proposed RTPP scheme is provable secure in the automatic privacy lifecycle tuning model for embedded systems and efficient in practice.

The detection of Thermal Power Plants (TPPs) is a meaningful task for remote sensing image interpretation. It is challenging due to the variations in appearance and complex structures. In this article, we propose a novel end-to-end detection framework for TPPs based on deep convolutional neural networks. Specifically, a large-scale TPPs Dataset for Detection (AIR-TPPDD) in remote sensing images is presented. AIR-TPPDD is collected from the Google Earth worldwide, and provides detailed annotations including names and locations. To the best of our knowledge, this is the first publicly available dataset for TPP detection. Then, based on Faster R-CNN, a saliency enhanced module is proposed to strengthen the ability in representing complex structure, as well as alleviate distractions in the background. In addition, we design a multi-scale feature module to adapt to the large size range of TPPs. Experiments show that the proposed method outperforms the state-of-the-art methods and achieves 76.7% mAP on the challenging AIR-TPPDD.

A monolithic two-section InGaAs/GaAs double quantum wells (DQWs) mode-locked laser (MLL) emitting at 1.06 μm is demonstrated. Stable mode locking operation is achieved up to 80 °C. The fundamental repetition rate is at ~9.51 GHz with a signal-to-noise ratio (SNR) of more than 55 dB, and up to the fourth harmonic at ~38.04 GHz is observed. The characteristic temperature (T0) of the laser and the influences of absorber bias voltage on T0 have been systematically investigated. From our findings, T0 shows a two-segment feature, and is slightly affected by the absorber bias voltage for photon saturation.

Phased array antennas have played a very important role in many different areas and applications. It requires precise excitation of each antenna element by various synthesis techniques to obtain desired array pattern features. However, due to reasons such as manufacturing imperfections, component aging, and temperature variation, the realistic antenna element excitation inevitably differs from their expected values in practice. This paper presents a tutorial-like review to deal with excitation errors for phased array antennas. Two kinds of analysis methods, probabilistic methods and interval arithmetic (IA) based methods, are presented to evaluate the effects of excitation errors for phased array antennas. State-of-the-art calibration methods along with various signal processing techniques are reviewed, their advantages and challenges are discussed in a comparative manner. Some other common errors and open research directions are also presented.

Small satellites in low Earth orbit (LEO) are typically organized as a satellite constellation. The satellite number of a LEO satellite constellation would be several hundred or even tens of thousands to realize a fully global coverage. This imposes a big challenge on ground station for satellite ordinary management and control operation. Phased array antennas are considered as attractive candidates for future satellite ground station. The primary reason is that phased array antennas can produce numbers of beams by the beamforming network thus support multiple satellites simultaneously. This paper aims to provide a tutorial-like review on phased array antennas as satellite ground station. The advantages of phased array antennas for this application are summarized by comparing them with traditional reflector antennas. Several array geometries for realizing the hemispherical coverage required for satellite tracking are presented. System architecture are given, and some considerations for engineering practice about phased array antenna based satellite ground station are also discussed.

In many practical problems, randomness and uncertainty simultaneously appear in one complex system or network. When graph theory is applied to these problems, these complex systems or networks are usually represented by uncertain random graphs, in which some edges exist with degrees in probability measure, and some other edges exist with degrees in uncertain measure. In this paper, we focus on the connectivity of uncertain random graphs with respect to vertices. We propose the concepts of k-vertex-connectivity index and the connectivity of an uncertain random graph. The former is the chance measure that an uncertain random graph is k-vertex-connected, and the latter is an uncertain random variable, which characterizes the connectivity of the uncertain random graph with respect to vertices. We discuss some properties of these concepts. Methods and formulas are also presented for calculating the k-vertex-connectivity index, and the distribution and expected value of the connectivity of an uncertain random graph.

Centralized publishing of big location data provides great convenience for various location-based interactive queries and services. Privacy protection of users' location information is an indispensable issue in the security of big data applications. Partition publishing is an effective way to release statistical information of two-dimensional big location data. By combining with the differential privacy model, it can provide more accurate range counting query service on the premise of ensuring location privacy. In order to further improve the availability of location data subsequent to centralized publishing, this paper analyzes the primary noise sources of partition publishing and discusses the constraints among publishing errors, the spatial partition structure, and privacy budget allocation. An unbalanced quadtree partition algorithm based on regional uniformity is proposed. Accordingly, the gradient privacy budget allocation scheme and adjustment method are designed to ensure the effectiveness of the differential privacy model. Experimental comparison of the real-world datasets proves the advantages of the proposed algorithm in improving the querying accuracy of the published data.

Reliable quantification of cellular treatment effects in many bioassays depends on the accuracy of cell colony counting. However, colony counting processes tend to be tedious, slow, and error-prone. Thus, pursuing an effective colony counting technique is ongoing, and varies from manual approaches to partly automated and fully automated techniques. Most fully automated techniques were developed using deep learning (DL). A significant problem in applying DL to this task is the lack of sizeable collections of annotated plate images. For this reason, here we propose an application of Transfer Learning to cell colony counting that can overcome this problem by exploiting models trained for other tasks. To demonstrate this idea's feasibility, we show how a small dataset can be used to transform a DL model designed for counting objects in congested scenes into a specialized cell colony counting model and achieve better performance than existing, more widely-used models.

Vehicle detection in aerial videos often requires post-processing to eliminate false detections. This paper presents a spatio-temporal processing scheme to improve automatic vehicle detection performance by replacing the thresholding step of existing detection algorithms with multi-neighborhood hysteresis thresholding for foreground pixel classification. The proposed scheme also performs spatial post-processing, which includes morphological opening and closing to shape and prune the detected objects, and temporal post-processing to further reduce false detections. We evaluate the performance of the proposed spatial processing on two local aerial video datasets and one parking vehicle dataset, and the performance of the proposed spatio-temporal processing scheme on five local aerial video datasets and one public dataset. Experimental evaluation shows that the proposed schemes improve vehicle detection performance for each of the nine algorithms when evaluated on seven datasets. Overall, the use of the proposed spatio-temporal processing scheme improves average F-score to above 0.8 and achieves an average reduction of 83.8% in false positives.

This paper elucidates the damage characteristics, microstructure response and temperature rise of steel alloy Q235B suffered from lightning currents, which is the basis for lightning protection of oil tanks. Damage is inflicted in the designed Q235B specimens by using the multi-waveform multi-pulse impulse current generator to characterize the damage response induced by four typical lightning current components. The changes in the element composition and micro-hardness of Q235B are documented in the experiment. The microstructure changes in response to the temperature rise are analyzed by the proposed lightning-metal temperature rise model. The results reveal that, first, return stroke current with an amplitude of 201.2 kA leads to the largest damaged area of 3523.8 mm2. Continuing current in the stroke intervals with a charge transfer of 12.1 C results in the damaged area of 12.6 mm2 and the damaged depth of 0.5 mm. Long continuing current after stroke with a charge transfer of 210.1 C leads to the deepest damaged depth of 3.0 mm. The demixing phenomenon occurs on the cross-section, forming the damage zone, transition zone, and origin zone. The damage zone mainly consists of martensite transferred from pearlite and ferrite at temperature 990 °C. The transition zone is mainly martensite and ferrite forming at temperature 900 °C. The hardness of the three zones is 450, 310, and 180, respectively. The damaged depth is only 0.001 mm caused by the subsequent return stroke current with an amplitude of 102.2 kA.

The planar three-link passive-active-active (PAA) under-actuated manipulator does not satisfy the small-time local controllability (STLC), which makes it more difficult to control than other planar under-actuated manipulators. This paper presents a two-stage switching hybrid control method based on improved particle swarm optimization (PSO) for the PAA under-actuated manipulator. According to the model reduction strategy, the control process is divided into two stages. The workspace of the manipulator is analyzed to guarantee the target position within the reachable region. In order to reduce the probability of falling into local optimum, we introduce the Metropolis criterion of simulated annealing algorithm and crowding factor of artificial fish swarm algorithm into PSO algorithm to calculate the target angles of two active joints. At each control stage, we design the Lyapunov function to maintain the angle of an active joint unchanged while we build the sliding surface and select the power function as reaching law to make the other active joint converge to the target angle. The control law switches to next stage when the rotated joint converges to the target angle. The proposed method not only retains the rapidity of sliding mode control, but also reduces the total chattering of the system by the Lyapunov function method. Simulation results demonstrate that the proposed method has shorter steady-state adjustment time than that of existing typical control methods, and its overshoot is almost zero.

Owing to the effectiveness in selectively focusing on regions of interest of images, the attention mechanism has been widely used in image caption task, which can provide more accurate image information for training deep sequential models. Existing attention-based models typically rely on top-down attention mechanism. While somewhat effective, attention masks in these attention-based models are queried from image features by hidden states of LSTM, rather than optimized by the objective functions. This indirectly supervised training approach cannot ensure that attention layers accurately focus on regions of interest. To address the above issue, in this paper, a novel attention model, Visual Aligning Attention model (VAA), is proposed. In this model, the attention layer is optimized by a well-designed visual aligning loss during the training phase. The visual aligning loss is obtained by explicitly calculating the feature similarity of attended image features and corresponding word embedding vectors. Besides, in order to eliminate the influence of non-visual words in training the attention layer, a visual vocab used for filtering out non-visual words in sentences is proposed, which can neglect the non-visual words when calculating the visual aligning loss. Experiments on UCM-Captions and Sydney-Captions prove that the proposed method is more effective in remote sensing image caption task.

Data releasing is a key part bridging between the collection of big data and their applications. Traditional methods release the static version of dataset or publish the snapshot with a fixed sampling interval, which cannot meet the dynamic query requirements and query precision for big data. Moreover, the quality of published data cannot reflect the characteristics of the dynamic changes of big data, which often leads to subsequent data analysis and mining errors. This paper proposes an adaptive sampling mechanism and privacy protection method for the release of big location data. In order to reflect the dynamic change of data in time, we design an adaptive sampling mechanism based on the proportional-integral-derivative (PID) controller according to the temporal and spatial correlation of the location data. To ensure the privacy of published data, we propose a heuristic quad-tree partitioning method as well as a corresponding privacy budget allocation strategy. Experiments and analysis prove that the adaptive sampling mechanism proposed in this paper can effectively track the trend of dynamic changes of data, and the designed differential privacy method can improve the accuracy of counting query and enhance the availability of published data under the premise of certain privacy intensity. The proposed methods can also be readily extended to other areas of big data release applications.

A weak voltage analog dimming method for high precision light emitting diode (LED) current control with N-Bits analog static offset calibration (N-Bits ASOC), dynamic offset cancellation (DOC) and K-times dimming duty to voltage (KD2V) conversion is presented. The N-Bits ASOC circuit decreases the analog dimming error to VOSMAX/2N - 1 by pre-calibrating the error amplifier offset. Moreover, through a smart combination of KD2V circuits and DOC amplifier, the KD2V and DOC operational amplifier magnify the dimming reference and LED current sense voltage K-times, respectively, which reduces the residual dimming error K-times without any modification to the LED current sampling resistor. Based on the 0.18μm 5V/40V Bipolar-CMOS-DMOS (BCD) process, experimental results for a cell phone backlighting driver show that the analog dimming ILED error reduces to 30μA at maximum 200mV LED current feedback voltage. This deviation mostly keeps constant through a dimming duty range of 0.5% to 100% and VIN range of 2.7V to 5.5V. Furthermore, a 200-sample measurement data suggests that this method is effective even when dimming duty is smaller than 0.5% for mass production.

RNAs and RNA-binding proteins (RBPs) in cells can bind with each other to form a nuclear ribonucleoprotein (RNP) complex, playing important roles in life processes, and gene regulation. How to accurately predict the RNA-binding proteins is a big challenge and hot research task. Here, we proposed a new computational predictor called iRBP-Motif-PSSM for identifying RNA-binding proteins by combining the motif information and the evolutionary information extracted from the Position Specific Scoring Matrixes. Collaborative Learning was employed to address the instability problem of the predictor. The experimental results showed that iRBP-Motif-PSSM showed better performance than other existing methods for identifying NA-binding proteins, indicating that iRBP-Motif-PSSM is a useful tool for biological analysis.

Disks are the main equipment for data storage in data centers. The prediction of disk failure is of great significance for the reliability and security of data. On account of the few abnormal samples in the disk datasets, it is difficult to satisfy the requirement of supervised and semi-supervised algorithms for the number of abnormal data while the unsupervised algorithms have poor performance on recall rate when solving the problems of local anomalies and wrapped a nomalies. This paper presents an incremental learning disk failure prediction model using the density metric of edge samples. An isolation region is built by searching the nearest neighbor of each sample. We calculate the nearest training point of the test point which is not a global anomaly and the nearest training point of the obtained nearest training point by Euclidean distance. The global metric of abnormal degree of the test sample comes from the ratio of the radius of the region where the two nearest training points are located. Then, the local metric of abnormal degree of the test sample comes from the ratio between the nearest distance from the test point to the edge of the training point region and the radius of the region. Abnormal scores of test points can be obtained by combining two measurements. We identify the SMART attributes that are significantly related to disk failures and promote their weights in the next time the attributes are inputted. The experiments are carried on the synthetic and public datasets which contain local anomalies and wrapped anomalies. The proposed method outperforms the typical unsupervised algorithms such as iNNE, iForest and LOF, and the achieved recall rates increase at most 7%. Furthermore, the contrast tests on the public disk datasets also verify the proposed method has better performance on recall rate.

The understanding on the response of aluminum alloy to direct lightning currents is needed for lightning protection. Four typical lightning current components, which are the first return stroke current, subsequent return stroke current, continuing current in the stroke intervals, and long continuing current, are simulated to investigate their damage effects on Al alloy 3003 material by laboratory experiments. The damage morphology on alloy's surface and the microstructure in cross section are observed with high-resolution scanning electron microscope. Elemental composition changes of alloy material are measured by an X-ray energy dispersive spectrometer. Indentation hardness tests are conducted to measure the changes in alloy's micro-hardness. Then, the damage characteristics and mechanisms of the alloy are analyzed by comparing the experimental results under different current parameters of lightning components. The results show that, in response to the first return stroke current component, a thin oxide layer and cracks are formed on the alloy's surface. The cracks are initiated by the broken of the oxide layer. Under the continuing current component in the stroke intervals, the dendritic crystals appear and grow in the direction of temperature decrease inside the material. In response to the long continuing current component, uneven solidification induced cracks are formed. The over-burn phenomenon also occurs and lots of hydrogen evolution porosities appear. As a response to the subsequent return stroke current, the damage pattern on alloy's surface is dominated by pits, and the thickness of the damaged layer is typically 15 μm.

Polyadenylation (Poly(A)) plays crucial roles in gene regulation, especially in messenger RNA metabolism, protein diversification, and protein localization. Accurate prediction of polyadenylation sites and identification of motifs that controlling polyadenylation are fundamental for interpreting the patterns of gene expression, improving the accuracy of genome annotation and comprehending the mechanisms that governing gene regulation. Despite considerable advances in using machine learning techniques for this problem, its efficiency is still limited by the lack of experiences and domain knowledge to carefully design and generate useful features, especially for plants. With the increasing availability of extensive genomic data sets and leading computational techniques, deep learning methods, especially convolutional neural networks, have been applied to automatically identify and understand gene regulation directly from gene sequences and predict unknown sequence profiles. Here, we present DeepPolyA, a new deep convolutional neural network-based approach, to predict polyadenylation sites from the plant Arabidopsis thaliana gene sequences. We investigate various deep neural network architectures and evaluate their performance against classical machine learning algorithms and several popular deep learning models. Experimental results demonstrate that DeepPolyA is substantially better than competing methods regarding various performance metrics. We further visualize the learned motifs of DeepPolyA to provide insights of our model and learned polyadenylation signals.