Fatigue driving has always received a lot of attention, but few studies have focused on the fact that human fatigue is a cumulative process over time, and there are no models available to reflect this phenomenon. Furthermore, the problem of incorrect detection due to facial expression is still not well addressed. In this article, a model based on BP neural network and time cumulative effect was proposed to solve these problems. Experimental data were used to carry out this work and validate the proposed method. Firstly, the Adaboost algorithm was applied to detect faces, and the Kalman filter algorithm was used to trace the face movement. Then, a cascade regression tree-based method was used to detect the 68 facial landmarks and an improved method combining key points and image processing was adopted to calculate the eye aspect ratio (EAR). After that, a BP neural network model was developed and trained by selecting three characteristics: the longest period of continuous eye closure, number of yawns, and percentage of eye closure time (PERCLOS), and then the detection results without and with facial expressions were discussed and analyzed. Finally, by introducing the Sigmoid function, a fatigue detection model considering the time accumulation effect was established, and the drivers’ fatigue state was identified segment by segment through the recorded video. Compared with the traditional BP neural network model, the detection accuracies of the proposed model without and with facial expressions increased by 3.3% and 8.4%, respectively. The number of incorrect detections in the awake state also decreased obviously. The experimental results show that the proposed model can effectively filter out incorrect detections caused by facial expressions and truly reflect that driver fatigue is a time accumulating process.

For the problem of 5G network planning, a certain number of locations should be selected to build new base stations in order to solve the weak coverage problems of the existing network. Considering the construction cost and some other factors, it is impossible to cover all the weak coverage areas so it is necessary to consider the business volume and give priority to build new stations in the weak coverage areas with high business volume. Aimed at these problems, the clustering of weak point data was carried out by using k-means clustering algorithm. With the objective function as the minimization of the total construction cost of the new base stations, as well as the constraints as the minimal distance between adjacent base stations and the minimal coverage of the communication traffic, the single-objective nonlinear programming models were established to obtain the layout of macro and micro base stations in order to illustrate the impact of the shape of the station coverage area, the circular and the “shamrock” shaped coverage areas were compared in this paper. For the “shamrock” base station, a secondary clustering was undertaken to judge the main directions of the three sector coverage areas. Then, an improved model taking the coverage overlapping into consideration was proposed to correct the coverage area of different sectors. Finally, the optimal layout was obtained by adjusting the distribution of all base stations globally. The results show that the optimal planning method proposed in this paper has good practicability, which also provides a very good reference for solving similar allocation problems of dynamic resources.

The problem of insufficient signal coverage of 5G base stations can be solved by building new base stations in areas with weak signal coverage. However, due to construction costs and other factors, it is not possible to cover all areas. In general, areas with high traffic and weak coverage should be given priority. Although many scientists have carried out research, it is not possible to make the large-scale calculation accurately due to the lack of data support. It is necessary to search for the central point through continuous hypothesis testing, so there is a large systematic error. In addition, it is difficult to give a unique solution. In this paper, the weak signal coverage points were divided into three categories according to the number of users and traffic demand. With the lowest cost as the target, and constraints such as the distance requirement of base station construction, the proportion of the total signal coverage business, and so on, a single objective nonlinear programming model was established to solve the base station layout problem. Through traversal search, the optimal threshold of the traffic and the number of base stations was obtained, and then, a kernel function was added to the mean shift clustering algorithm. The center point of the new macro station was determined in the dense area, the location of the micro base station was determined from the scattered and abnormal areas, and finally the unique optimal planning scheme was obtained. Based on the assumptions made in this paper, the minimum total cost is 3752 when the number of macro and micro base stations were determined to be 31 and 3442 respectively, and the signal coverage rate can reach 91.43%. Compared with the existing methods, such as K-means clustering, K-medoids clustering, and simulated annealing algorithms, etc., the method proposed in this paper can achieve good economic benefits; when the traffic threshold and the number of base stations threshold are determined, the unique solution can be obtained.

In this study, the micro-explosion and combustion characteristics of a multi-component fuel droplet cluster are studied using a magnified high-speed backlit illumination technique. The multi-component fuel blends consist of n-butanol and n-pentanol as the low-boiling-point component, while biodiesel is used as the high-boiling-point constituent. The range of turbulence intensity is 0–0.72 m/s, and dimensionless droplet spacings (s/d0) are 3 and 5. The results indicate that the flames of droplet clusters exhibit significant fluctuations under turbulent conditions, especially at a spacing of 3. Furthermore, turbulence significantly accelerates the flame spread of droplet clusters at larger spacings. The flame spread time at 0.72 m/s is reduced by 51.5% compared to the stationary state at a droplet spacing of 5. The micro-explosion frequency of droplets significantly increases with the increase in turbulence intensity when the droplet spacing is 3, while the change is relatively small when the droplet spacing is 5. The interaction coefficient between droplets is usually less than 1 at small droplet spacing, indicating that turbulence weakens the combustion rate of droplet clusters. However, the interaction coefficient is significantly higher at larger droplet spacings than at smaller spacings. Therefore, droplet clusters in turbulent environments are more conducive to combustion at relatively large droplet spacings.

The stability of unmanned aerial vehicles (UAVs) during propulsion failure remains a critical safety challenge. This study presents a center-of-mass (CoM) correction device, a compact, under-slung, and dual-axis prismatic stage, which can reposition a dedicated shifting mass within the UAV frame to generate stabilizing gravitational torques by the closed-loop feedback from the inertial measurement unit (IMU). Two major experiments were conducted to evaluate the feasibility of the system. In a controlled roll test with varying payloads, the device produced a corrective torque up to 1.2375 N·m, reducing maximum roll deviations from nearly 90° without the device to less than 5° with it. In a dynamic free-fall simulation, the baseline UAV exhibited rapid tumbling and inverted impacts, whereas with the CoM system activated, the UAV maintained a near-level attitude to achieve the upright recovery and greatly reduced structural stress prior to ground contact. The CoM device, as a fail-safe stabilizer, can also enhance maneuverability by increasing control authority, enable a faster speed response and more efficient in-air braking without reliance on the rotor thrust, and achieve comprehensive energy saving, at about 7% of the total power budget. In summary, the roll stabilization and free-fall results show that the CoM device can work as a practical pathway toward the safer, more agile, and energy-efficient UAV platforms for civil, industrial, and defense applications.