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With the trend of Industry 4.0, the global machine tool industry is developing towards smart manufacturing. The ball bearing is a key component of the rotary axis of machine tool, and its functionality is to bear the external load on the axis as well as maintain the center position of the axis. A damaged bearing will result in abnormal vibration and noise, and thus will lead to the damage of the machine and produced workpieces. Therefore, inspection and identification of ball bearing failures is particularly important. This paper discusses the fault signals of ball bearings published by the Society for Machinery Failure Prevention Technology (MFPT) and creates a recognition model for the ball bearing state based on different fault states, and then we adopt two different approaches for feature extraction. The first approach implements Finite Impulse Response Filter (FIR) and Approximate Entropy (ApEn) to extract the signal features. The second approach utilizes the Chen-Lee chaotic system for analysis and takes its chaotic attractor as the feature of the state recognition. The comparison of model recognition accuracy for Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and K Nearest Neighbor (KNN) was conducted after acquiring the features through the two approaches in this paper. The results of the experiments in this paper show that both of the feature extraction approaches enable the state to be recognized easily. The Chen-Lee chaotic system with BPNN not only reaches 100% identification rate and it has the highest overall efficiency; it takes only 0.054 second to complete the feature extraction for 63 sets of data; this study is able to provide timely and precise solution for the failure of key mechanical components.

A critical bottleneck of massive multiple-input multiple-output (MIMO) system is the huge training overhead caused by downlink transmission, like channel estimation, downlink beamforming and covariance observation. In this paper, we propose to use the channel state information (CSI) of a small number of antennas to extrapolate the CSI of the other antennas and reduce the training overhead. Specifically, we design a deep neural network that we call an antenna domain extrapolation network (ADEN) that can exploit the correlation function among antennas. We then propose a deep learning (DL) based antenna selection network (ASN) that can select a limited antennas for optimizing the extrapolation, which is conventionally a type of combinatorial optimization and is difficult to solve. We trickly designed a constrained degradation algorithm to generate a differentiable approximation of the discrete antenna selection vector such that the back-propagation of the neural network can be guaranteed. Numerical results show that the proposed ADEN outperforms the traditional fully connected one, and the antenna selection scheme learned by ASN is much better than the trivially used uniform selection.

A questionnaire survey was conducted among 1659 college students to investigate the mediating role of loneliness between stressful life events and college students’ depression, at the same time, the moderating effect of perceived organizational support on the mediating mechanism was tested. The results show that: (1) there is a positive correlation between stressful life events and depression; (2) stress life events also indirectly affect college students’ depression through the mediating effect of loneliness; (3) perceived organizational support plays a moderating role between stressful life events and loneliness. With the increase of perceived organizational support, the positive predictive effect of stressful life events on loneliness is weakened; (4) perceived organizational support further moderates the mediating role of loneliness between stressful life events and college students’ depression. The results of the study have a certain guiding role in enriching the related research of College Students’ stress and depression, as well as the prevention and intervention of College Students’ stress and depression.

Code comments are key to program comprehension. When they are not consistent with the code, maintenance is hindered. Yet developers often forget to update comments along with their code evolution. With recent advances in neural machine translation, the research community is contemplating novel approaches for automatically generating up-to-date comments following code changes. CUP is such an example state-of-the-art approach whose promising performance remains however to be comprehensively assessed. Our study contributes to the literature by performing an in-depth analysis on the effectiveness of CUP. Our analysis revealed that the overall effectiveness of CUP is largely contributed by its success on updating comments via a single token change (96.6%). Several update failures occur when CUP ignores some code change information (10.4%) or when it is otherwise misled by additional information (12.8%). To put in perspective the achievements of CUP, we implement HEBCUP, a straightforward heuristic-based approach for code comment update. Building on our observations on CUP successful and failure cases, we design heuristics for focusing the update on the changed code and for performing token-level comment update. HebCup is shown to outperform CUP in terms of Accuracy by more than 60% while being over three orders of magnitude (i.e., 1700 times) faster. Further empirical analysis confirms that the HebCup does not even overfit to the empirical analysis set. Overall, with this study, we call for more research in deep learning based comment update towards achieving state-of-the-art performance that would be unreachable by other less sophisticated techniques.

Chatter, the vibration between the workpiece and cutting tool, is a common phenomenon during the milling process. Traditionally, the Fourier transform analysis is mainly used to extract the features and determine whether chatter occurs. In this paper, an innovative and practical chatter identification method combining fractional order chaotic system and extension theory is proposed. A lathe spindle with embedded sensors is used in this study. The boundary of chattering state of the lathe spindle is decided by the center of gravity of phase plane of dynamic errors. The boundaries are then fed into extension model and relational function calculation is performed. In this way, chatter identification can be easily achieved based on the position of the chaotic center of gravity. The three chaotic systems, i.e. Lorenz, Chen-Lee, and Sprott, of different fractional orders are used and their results are compared. Compared with the traditional methods, the Fourier transform is time-consuming in terms of mathematical operations and adverse to the establishment of a real-time system. This paper uses the characteristics of the chaotic systems sensitive to input signals in order to more capably detect the boundary state from normal cutting to chattering cutting and more efficiently identify the chatter. The experiment results indicate that the Chen-Lee system (93.5%) exhibits have better chatter diagnosis rate than Lorenz (92.75%) and Sprott (69%) systems. The Chen-Lee system even reaches a diagnosis rate of 100% for orders 0.5 ~ 0.7. Therefore, the method presented in this paper has a very high diagnosis rate and is thus very effective for chatter identification of machine tools.

Research on emotion recognition has recently started to gain increased attention. Inner emotions or thought activity can be determined by analyzing facial expression, behavioral responses, audio, and physiological signals. Facial expression is now recognized as an important form of non-verbal interaction. In this paper, emotion-specific activation maps were constructed to establish infrared thermal facial image sequences as an alternative approach to the determination of the correlation between emotional triggers and changes in facial temperature. During the testing process, data stored in the International Affective Picture System were used to create emotional clips that triggered three different types of emotion in the subjects, and their infrared thermal facial image sequences were simultaneously recorded. For processing, an image calibration protocol was first employed to reduce the variance produced by irregular micro-shifts in the faces of the subjects, followed by independent component analysis and statistical analysis protocols to create the facial emotional activation maps. The test results showed that the problem of selecting local regions when analyzing frame temperature had been resolved. The emotion-specific facial activation maps provide visualized results that facilitate the observation and understanding of information.

In this letter, we propose an indoor visible light positioning technique that combines deep neural network based on the Bayesian Regularization (BR-DNN) with sparse diagonal training data set. Unlike other neural networks, which require a large number of training data points to locate accurately, we realize the high precision positioning with only 20 training points in a 1.8 m × 1.8 m × 2.1 m location area. Furthermore, we test a new optimization method of training data set, which is the diagonal set. To verify our ideas, we experimentally demonstrate three different training data acquisition methods that contain the common choice of training points (even set), arbitrary selection (arbitrary set), and diagonal selection (diagonal set). Experimental results show that the average localization accuracy optimized by the BR-DNN is 3.40 cm with the diagonal set, while the average localization accuracy is 4.35 cm for the arbitrary set and 4.58 cm for the even set. In addition, the training time and positioning time are only 11.25 and 8.66 ms due to a significant reduction of the sparse training data. All of the aforementioned experimental results show that the algorithm and training data optimization we proposed provide a new solution for real-time and high-accuracy positioning with the neural network.

Automated Program Repair (APR) is becoming a hot topic in Software Engineering community with many approaches being proposed and experiments being performed over the years. The results obtained from different experiments can be used as practical guidance to advance APR techniques. However, researchers have generally ignored the biases with respect to the unexpected results generated by various APR techniques, in which case the repair process cannot be finished normally and is terminated with unexpected exceptions (referred to as the non-repairability factors). In this paper, we aim to thoroughly understand the reasons for such non-repairability factors of various APR techniques, thus to provide practical insights for diverse stakeholders to establish an unbiased evaluation of APR techniques. To achieve so, we performed a systematic study on the existing execution logs that are ended with unexpected exceptions collected from different APR studies. Specifically, we investigated different types of exceptions with their frequencies of occurrence, the behind reasons of such occurrences, as well as the impact of such exceptions on the repairability of APR techniques. Our experimental results reveal that: 1) non-repairability factors happen in 25.7% of our studied logs and are widespread among diverse combinations of APR tools with FL strategies; 2) Inherent defect of APR tools is the most common reason for the occurrence of the non-repairability factors; 3) the impact of the non-repairability factors on the performance of APR tools can be rather significant. Our empirical study indicates that it is of great importance to eliminate the biases from the non-repairability factors. We also highlight several implications for actions that we can take to eliminate such biases.

From the perspective of global level on digital Earth and Earth big data, this paper elaborated the development process of previous Antarctic expeditions on digitization and big data of products (services) provided by the National Center for Marine Environment Forecasting of China in the past 36 years, including marine route monitoring and forecasting guarantee, "sea-land investigation" monitoring and forecasting guarantee, traffic monitoring and forecasting guarantee for harbor unloading and "land-air storage and transportation" of investigation ship (team); in which, this paper elaborated the source and role of forecasting big data on cruising observation, localized observation, station area observation, mobile observation, as well as big data reanalysis of satellite remote sensing information, besides, the practical effects of various products were elaborated; moreover, the briefly introduction of the construction progress and work status of scientific investigation stations in Antarctica, especially meteorological observation and forecast stations in China was given. Main achievements: Firstly, creatively completion of 36 batches of marine and atmospheric forecast and guarantee tasks for Antarctic expeditions in China; secondly, establishment of the environmental monitoring and forecasting guarantee system of "sea-air" for several maritime routes of Antarctic expeditions in China, that of "sea-land-air" for the surrounding Antarctic continent, and that of "land-air" for the Antarctic bay to the inland depth hinterland.

This paper presents a 5-order low-pass filter (LPF) structure based on the operational transconductance amplifier(OTA) structure that is designed with low noise and HD3. To ensure that the filter has a lower power dissipation, noise, and third harmonic distortion(HD3), the OTA operates in weak inversion region and uses technology including current division, current cancellation, source degeneration. The LPF is simulated by the 0.18 µm CMOS process, and the results show that the LPF locates within 250Hz and consumes 65.49 µW, the ripple is less than 0.2dB, and the HD3 is less than -70.21dB (@50mV). The maximum input reference noise (IRN) is $71.86\ \mu V/\sqrt{Hz}$ The proposed LPF is suitable for Electrocardiograph (ECG) signal detection analog front circuit.

In this paper, the embedded system and inverted pendulum are proposed as commonly research area. PID controller as tuner, optimized by using taguchi method to build control design from DC motor. Further search proportional-derivative parameters eliminating with the taguchi method using analysis of means. The inverted pendulum (IP) control system design has become a various optimal control in engineering for researchers due to the important application of IP in practical life. There are many controller types Like: PID, fuzzy controller, LQR controller and any else. The controller parameters could affect the system stability. However, the taguchi method used by the author as optimization in tuning PID controller. The taguchi method proves its fine-tuning through enhancing the stability system. The results of investigation are verified via test for random disturbance applied on the embedded system.

Spatial diversity can be used over satellite-to-ground optical links to mitigate turbulence induced fading. In this paper, bit-error rate (BER) performances of satellite-to-ground downlink optical communication are evaluated for fiber-based coherent detection receiver system with spatial diversity. The performance analysis is carried out with M-ary phase shift keying (M-PSK) and M-ary quadrature amplitude modulation (M-QAM) schemes. Maximum ratio combining (MRC), equal gain combining (EGC), and selection combining (SC) are evaluated as diversity combining methods. We consider Gamma-Gamma distributed model for the signal irradiance and non-central chi-square distribution for the random fluctuation of fiber-coupling efficiency. BER performances of fiber-based coherent detection receiver system with downlink spatial diversity are analyzed for 8PSK and 8QAM modulation schemes and compared with receiver system with single aperture under the same conditions. BER performances of fiber-based receiver systems with spatial diversity are also studied for MPSK and MQAM with different constellation M. It is observed that 8PSK and 8QAM modulated fiber-based receiver systems with spatial diversity can significantly improve the BER performance with EGC and MRC receiver system. For SC diversity, additional number of apertures does not mean better communication performance under the case of low zenith angles. The research work of this paper has laid a analytical foundation for the application of satellite-to-ground downlink multiple apertures receiver system with fiber-based coherent detection MPSK and MQAM modulations.

In a geometric mechanics framework, the configuration space is decomposed into a shape space and a position space. The internal motion of the system is prescribed by a closed loop in the shape space, which causes net motion in the position space. If the shape space is a simply connected domain in an Euclidean space, then with an optimal choice of the body frame, the displacement in the position space is reasonably approximated by the surface integral of the height function, a functional relationship between the internal shape and position space variables. Our recent work has extended the scope of geometric methods from limbless undulatory system to those with legs; interestingly, the shape space for such systems has a torus structure. However, to the best of our knowledge, the optimal choice of the body frame on the torus shape space was not explored. In this paper, we develop a method to optimally choose the body frame on the torus which results in good approximation of displacement by the integral of the height function. We apply our methods to the centipede locomotion system and observe quantitative agreement of our prediction and experimental results.

Based on the problem of non-matching of protective characteristics before and after the use of solid-state power distribution, this paper proposes a stress assessment model in the short-circuit mode of solid-state electronic switch, and realizes the calculation method of transient junction temperature of power semiconductor devices in the short-circuit mode. The parasitic parameters and influencing factors of the solid-state power distribution circuit are analyzed based on the whole satellite state. The solution of the front and back level protection characteristics matching is given to ensure the safety of the solid-state power distribution aerospace application, which has a strong guiding significance and application value.

This thesis introduced the construction and development process of high-performance computing of marine forecasting from scratch, from small to large, from weak to strong by National Marine Environmental Forecasting Center of the Ministry of Natural Resources of the People's Republic of China in the past half century; expounded the “two milestones”: zero breakthrough of high-performance computing of marine forecasting and computing power exceeding 1 billion times per second for the first time from the early 1970s to the late 1990s in high-performance computing of ocean forecasting, and the achievements of ocean forecasting on the journey of globalization, refinement and high resolution with the help of high-performance computing after entering the 21st century; studied, designed and showed a better architecture blueprint for the next generation of the high-performance computer system for marine forecasting; pointed out that there were gaps between the high-performance computing of marine forecasting and the international advanced one, domestic advanced one and professional requirements, and put forward two suggestions to enhance the computing capability of existing equipment through technological innovation and scientific research innovation from the perspective of professional technology; and finally held that there was still a long way to go to build China's high-performance computing power for marine forecasting in comparison with the medium and long-term objectives and tasks of the modernization of marine forecasting.

This paper describes the process to build marine observation data sources of the National Marine Environment Forecast Center of the Ministry of Natural Resources through various channels, which has been relying on China’s national basic marine observation system since 2013 and taking the western route of the Maritime Silk Road as the target through a variety of diplomatic channels and international cooperation channels, as well as scientific and technological cooperation and information technology methods. The data sources on the western route of the Maritime Silk Road are in a chain structure, including South China Sea ocean observation system which is autonomous, perfect and efficient, North Indian Ocean observation network which is autonomous, international cooperated, sharing, exchange with other forms of integration and initial formation system; rapid developing Mediterranean ocean observation system and loose Atlantic Ocean observation framework. This vast ocean observation data source project has not only made an important contribution to the disaster prevention of the Maritime Silk Road, but also laid a solid data foundation for the development of China’s global ocean forecasting technology. There is no end to the Maritime Silk Road, and the exploration of the ocean observation data sources for the Maritime Silk Road is endless.

Aiming at the fast and high-precision DOA estimation problem on CACIS-type array, this paper proposes a fast-RVM algorithm which can be used for complex data. Firstly, the CACIS-type array structure is analyzed. Secondly, based on the virtual array expansion principle, a sparse signal model based on complex data is constructed. Finally, the real data processing is performed to make it applicable to the data structure of the fast-RVM algorithm. The simulation results show that the proposed method has ideal effects in both direction finding accuracy and computational complexity.

A diplexer with different-frequency power dividers cascaded to its output ports can split a signal into different frequency bands for power dividing functions. However, the design of a diplexer usually requires two different frequency filters. This paper presents three new microstrip diplexing power dividers (DPDs) to achieve duplexing power division without requiring additional filters. Moreover, to increase selectivity, each of the proposed DPDs has the flexibility of adding a dual-band filter to its input port. In particular, the proposed DPDs have matching circuits integrated in the dividers, enabling each divider with imaginary input impedance to reach the required open condition; this is achieved by designing a 50- $\Omega $ transmission line of appropriate length or a quarter-wavelength ( $\lambda $ /4) transmission line.