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Fuel cell vehicles have rapidly occupied the market with advantages such as environmental protection and energy conservation. However, their battery technology is insufficient and their endurance is poor, making them unsuitable for use over long distances. To address the aforementioned issues, a fuel cell vehicle energy storage system based on super-capacitors was constructed. Meanwhile, a proportional integral derivative controller based on fuzzy algorithms was established. Finally, the particle swarm optimization algorithm was used to optimize the fuzzy control strategy that integrated the fuzzy algorithm. When using the optimized fuzzy control strategy for simulation, the peak power of the fuel cell output power was reduced from 3.8kW to 2.0kW. The remaining power of the super-capacitor remained stable within a reasonable range throughout the entire operating condition. Under the new European urban road cycle, the optimized control strategy improved energy recovery performance by 4.3% and reduced hydrogen consumption by 0.9964%. Under the United States federal environmental protection agency standardized urban cycle conditions, the optimized control strategy improved the braking energy recovery efficiency index and effective braking energy recovery efficiency by 8.9% and 6.3%, respectively. The percentage reduction in hydrogen consumption was 0.9433%. Therefore, this research method can effectively reduce hydrogen consumption and improve the product economy and market competitiveness of enterprises.

Faced with complex and ever-changing environmental conditions in the agricultural field, efficient agricultural information gathering is crucial for optimising agricultural output. Therefore, a new path planning algorithm combining ant colony algorithm and particle swarm optimization is proposed in this study. The aim is to achieve fast and accurate path planning for agricultural information gathering robots in diverse agricultural environments. The global search ability of particle swarm optimization algorithm in finding optimal paths and the local search advantage of ant colony algorithm in obstacle avoidance are used to optimise the movement strategy of robots in agricultural environments. The research results showed that the global path planning distance of this method was 19.328m. The execution time was 0.97s. In local path planning, the proposed algorithm had a fitness function value of 30.123 when the number of iterations reached 53. In mixed path planning, the proposed algorithm reduced the movement time by 3.2s. The conclusion shows that the algorithm proposed in this study has high applicability and efficiency in practical applications, providing an effective strategy for path planning of agricultural information gathering robots. It has important practical significance for promoting the development of intelligent agriculture.

Code fragment natural language description generation, also known as code summarization, refers to obtaining a natural language sequence describing a given code fragment's functionality. It is broadly agreed that applying code summarization into production can significantly improve the efficiency of software development and maintenance. In recent years, syntactic analysis (SA) technology and Latent Dirichlet Allocation (LDA) has been widely used in code summarization and has achieved good results. However, most of the existing techniques focus on core code statements, and thus their generated code summarization lacks a logical description of the code fragment's holistic information. To this end, we propose a code summarization method based on multiple modules to generate natural language for each code statement by constructing a new type of natural language template. Meanwhile, to utilize the code fragment's holistic information, we adopt the code statement partition rules and cosine similarity measure to rank and optimize the weight of the overall information of the code fragment, and finally generate the holistic natural language description of the code fragment. The experimental results demonstrate that our method can generate more concise and logical natural language descriptions than existing models.

Polarization modulation imaging technology plays an important role in microscopic super-resolution imaging. However, the specimen medium contains retardancy, while charge-coupled devices may provide discrete under-sampling, and the coupled wavefronts consisting of the polarization state of the light and the anisotropic distribution of the specimen can lead to vectorial phase fitting degradation. Considering that the point spread function (PSF) of the main degradation parts can be regarded as an asymmetric generalized Gaussian distribution with uncertain parameters, an adaptive image reconstruction method is proposed based on variable exponential function regularization. The proposed method concentrates on the diversity of the PSF and uses a variable exponent regularization to improve flexibility of the kernel. Moreover, it can balance image edge preservation and provide staircase artifact suppression, which reduces the over- and under-reconstruction of the microscopic images effectively. By optimizing the Split–Bregman algorithm, we create an efficient method that minimizes the iterative loss function under the premise of achieving high estimation accuracy. Compared with other methods, the experimental results reveal better effectiveness and robustness of the proposed method, with improvements of 18% in the peak signal-to-noise ratio, 21% in the structural similarity index measurement, and 337% in the mean structural similarity index measurement.

Communication systems deployed in harsh environments, such as high-speed trains, subways, deserts, trenches, forests, and underground mines, should be specially constructed to withstand extreme conditions such as high or low temperatures, corrosive humidity, extreme weather, or excessive dust and dirt. Such applications require specially designed wireless communication, fiber-optic communications, satellite communication, or signal processing techniques that can perform under extreme conditions and meet the QoS, security, and reliability requirements. Quite often, communications in harsh environments have an extremely low signal-to-noise ratio (SNR), high Doppler shift, and long latency, and often consume more power and energy. Bandwidth limitation in harsh environments requires spectrum efficient communications. The current state of art technologies such as massive MIMO, advanced modulation and channel coding, artificial intelligence, and signal processing provide different venues to explore this challenging area.

Image dehazing algorithms based on dark channel prior principle have achieved good results for most scenes. However, the popular dark channel prior tends to underestimate transmissions of bright areas or objects, such as the skies, white areas and self-luminous bodies, which may cause color distortions during dehazing. A complementary prior called the extreme reflectance channel prior (ERC), which combines the dark channel prior with the bright channel prior, is proposed to estimate the transmission map. The extreme reflectance channel is the union of dark and bright channel’s pixels which satisfy the corresponding channel. Based on the scattering analysis results that the intensities of pixels in ERC are often close to 0 or 1 for the natural haze-free images or close to global atmospheric light if hazes occur in the air, the pixels in a hazy image can be recovered according to ERC to calculate the transmission map and then solve the haze imaging mode. Experiments show that ERC method outperforms state-of-the-art methods in PSNR, SSIM and visual perception effects.

Deep learning has been a widely adopted approach to achieve the remaining useful life prediction (RUL) of rolling bearing. However, the architectures of the current proposed deep learning approaches are limited and the prediction result is less stable on account of the single sensory data adopted. To address this issue, a new cascade fusion cascade convolutional long-short time memory network is proposed for bearing RUL prediction, in which a cross connection block is formulated to fuse the information streams from the adjacent channels twice and a concentration operation is also affiliated in the end of the network to integrate the separated information streams into an ensemble form. Meanwhile, a convolutional long-short time memory network is adopted as the basic cell in the proposed network on account of its ability to reflect the spatial-temporal correlation of the representative features. Moreover, a smoothing method based on multi-averaging operation is constructed in the prediction phase to largely eliminate the fluctuation in the prediction results. The application on the experimental bearing degradation dataset is able to verify the superiority and stability of the proposed method in comparison with the other comparison methods.

In this paper, a metasurface-based coupling reduction method is presented with the advantage of low profile for the MIMO antenna array. Unlike other metasurface-based decoupling technologies, this proposed metasurface for decoupling is loaded in the same layer as the coupled two-element patch antennas. Hence, the profile of the proposed decoupled array is greatly reduced compared to that of other published metasurface-based decoupled arrays. In this design, the two patch antennas with a short edge-to-edge distance of approximately 0.06 λ are surrounded by the period split ring resonator (SRR) elements. Next, two prototypes are fabricated; the test results show that the isolation performance for the decoupled array is better than that of a coupled array without SRRs, and the value of IS21I improves from -8 dB to -25 dB in a wide band of 5.0-6.0 GHz with IS21I

Vehicular fog and cloud computing (VFCC) system, which provides huge computing power for processing numerous computation-intensive and delay sensitive tasks, is envisioned as an enabler for intelligent connected vehicles (ICVs). Although previous works have studied the optimal offloading scheme in the VFCC system, no existing work has considered the departure of vehicles that are processing tasks, i.e., the occupied vehicles. However, vehicles leaving the system with uncompleted tasks will affect the overall performance of the system. To solve the problem, in this paper, we study the optimal offloading scheme that considers the departure of occupied vehicles. We first formulate the task offloading problem as an semi-Markov decision process (SMDP). Then we design the value iteration algorithm for the SMDP to maximize the total long-term reward of the VFCC system. Finally, the numerical results demenstrate that the proposed offloading scheme can achieve higher system reward than the greedy scheme.

Lane changing (LC) is the basic maneuver of a variety of lateral control scenes such as overtaking, paralleling, collision avoidance, etc. The surrounding vehicle may drive into the planned path during the ego-vehicle's LC maneuver, the ego-vehicle could only stop or fail to respond in time. Aiming to tackle this issue, this article proposes a LC strategy with real-time obstacle avoidance function. Firstly, the common LC strategy adopts the single planning and real-time tracking framework, which cannot handle emergent situations during the LC maneuver. This article proposes a new framework of real-time planning and real-time tracking for obstacle avoidance in the process of the LC. Secondly, this article constructs a two-layer judgment condition and control mode matrix for the two obstacle avoidance conditions of current-lane preceding-vehicle lane-change (CPC) and target-lane preceding-vehicle deceleration (TPD). Finally, experiments and simulations demonstrate the effectiveness of the proposed algorithm.

Degradation reliability analysis incorporating two-phase or even multi-phase features has become a research focus in recent years. Motivated by the fact that a practical multi-phase degradation procedure may probably involve both linear phases and nonlinear phases, a multi-phase degradation model is constructed that can consider the phase-varying nonlinear property. The unit-to-unit variability is incorporated. Meanwhile, the reliability function based on the first passage time (FPT) conception is derived, where the solution for the two-phase situation is first obtained, and the one for the multi-phase circumstance is constructed via a reclusive scheme. To verify the reasonability of the proposed reliability function, a simulation study has been complemented. Then two real applications are given to demonstrate the validity and the effectiveness of the proposed method.

To properly depict the heteroscedasticity when analyzing strain-based fatigue reliability, this paper proposes a novel P-ε-N model from both viewpoints of geometric properties and statistical characteristics. The constructed approach deals with the elastic strain and plastic strain separately, where the logarithmic fatigue life is considered as normally distributed with linear mean and standard deviation (std) functions of logarithmic elastic strain or plastic strain, respectively. P-ε-N curve with any percentile can be derived to facilitate the reliability analysis. An improved parameter estimation method based on maximum likelihood estimation (MLE) via genetic algorithm (GA) is further established. The presented method is illustrated and verified via a comprehensive simulation study and two real applications by comparing with the conventional homoscedasticity model. Corresponding results indicate that our proposed method can enhance the modeling accuracy and provide an extensive adaptation.

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) utilizes large antenna arrays and is considered a promising technology for fifth-generation (5G) and beyond wireless communication systems. However, the high-power consumption of the radio-frequency (RF) chains makes it infeasible. To solve this problem, hybrid precoding is proposed, which is a combination of analog and digital precoding. The fully connected architecture hybrid precoding still requires a large number of phase shifters (PSs). The sub-connected architecture can greatly reduce the required power consumption, and however, it cannot obtain a satisfactory achievable rate. To avoid the high energy consumption and obtain a high resolution, we propose a novel partly connected architecture in this paper. In addition, we propose an energy-efficient successive interference cancelation (SIC) hybrid precoding based on the partly connected architecture, which transforms the problem of maximizing the total achievable rate with non-convex constraints into a series of sub-rate optimization problems. Furthermore, a low-complexity energy-efficient SIC hybrid precoding based on the partly connected architecture is developed, which uses the partial singular value decomposition (SVD) to realize the sub-rate optimization and significantly reduce the complexity. Theoretical analysis demonstrates the superiority of the proposed hybrid precoding in terms of complexity. The simulation results indicate that the proposed hybrid precoding algorithms enjoy better energy efficiency and achievable rate performance than some recently proposed hybrid precoding algorithms.

A novel closed-form and stochastic internal wall insertion loss model (IWIL) in the indoor- indoor channel is proposed in this paper. The IWIL (dimensionless) is modeled as a generalized beta prime distributed random variable on the basis of the Nakagami fading. The probability distribution function (PDF), expectation, and standard deviation of IWIL are derived based on the proposed model. The impacts of the Nakagami-m parameters on the expectation and standard deviation of IWIL are also analyzed. Extensive IWIL measurements at 3.5, 6, and 11 GHz are carried out to validate the proposed model. Both Kolmogorov- Smirnov and Chi-square tests are exploited to determine the goodness of fitting between the modeled and measured data. Results show that the modeled PDF provides a better fit to the measured PDF than that of the Log-normal distribution. The proposed model can be used for the dense small cell networks in the future fifth generation wireless communication.

This paper investigates the capacity of the cooperative cellular networks and proposes to use the 2-D nested deployment on the base stations. Further analysis of sum-rate capacity and spectrum efficiency shows that this approach provides significant improvement. The study leverages the traditional model of regarding the coverage of base stations as the hexagonal blocks and views each hexagon as a macro-cell. One of the theoretical contributions of this paper is to use the invariance in the difference co-array to cover the derive the entire set of the channel fading coefficients. Furthermore, this paper proves the validity of using the average sum-rate capacity to calculate the capacity of the networks in both Rayleigh fading and nonfading channels. We also use simulations to confirm the correctness of the proposed theories from another perspective. As a result, the proposed approach remarkably improves the capacity of the network with the same amount of base stations.

This paper assesses the performances of a downlink cooperative non-orthogonal multiple access (C-NOMA) network over Nakagami-m fading channels. The analytical outage probability expression for each user is derived and verified by Monte Carlo simulations. Based upon the outage probability, the maximal throughputs are obtained and analyzed under different channel conditions. Furthermore, the effects of channel fading on the probability and throughput are investigated. The results indicate that the performance of the C-NOMA system is excellent over the source-relay link with less fading. This analysis can be used for designing the C-NOMA systems in the future fifth-generation wireless communication.