In this study, a dual-piezoelectric energy harvesting system with contact and non-contact characteristics was driven by a cantilever beam. The harvester performance of the multipoint energy harvesting system driven by cantilever-beam vibration was designed, detailed analysis and optimization strategies were developed, and its application in the security field was successfully demonstrated. Herein, we provide theoretical guidance for the design of the dual-piezoelectric energy harvesting. We designed and fabricated a prototype of the dual-piezoelectric energy harvesting. A test system was designed and constructed. The relationships among the distance and frequency of the two piezoelectric acquisition mechanisms and the open-circuit voltage were investigated. Additionally, the effects of different loads on the output power were examined. The peak power reached 10.12 mW under a gravitational acceleration of 1g. The analysis indicated that the dual-piezoelectric energy harvest device has a higher energy harvest efficiency than the single-piezoelectric energy harvest device. Owing to the multipoint harvest strategy, even if a generator suddenly deteriorates or fails, the entire system can maintain a certain power output, which is more commercially feasible. The results of this study indicate that the output of the piezoelectric energy harvesting is stable and reliable and that the output energy satisfies the requirements for a safety warning device.

The output voltage of inverters is influenced by nonlinear factors such as dead time and voltage drops, injecting low-order harmonics. This results in fifth and seventh harmonic distortions in the stator current, causing periodic torque ripples and significantly affecting the control precision of Permanent Magnet Synchronous Motors (PMSMs). To address this issue, this paper proposes a control strategy named quasi-proportional-resonant sliding mode control (QPR-SMC). Initially, sliding mode control is employed as the current controller to enhance disturbance rejection capability and provide a rapid dynamic response. Subsequently, a quasi-proportional-resonant controller is introduced to extract the sixth harmonic component from the current, which is then used as a compensation term for the sliding mode control surface. Finally, the current tracking error and the compensation term are combined as inputs to the sliding mode control law, forming a current error-proportional resonant-sliding mode control surface. This approach enhances the harmonic suppression capability of the system. The results demonstrate that the proposed method effectively reduces the fifth and seventh harmonic components in the three-phase current and mitigates motor jitter by suppressing the sixth harmonic in the d–q coordinate system.

In order to solve the problem of low response frequency and poor consistency of conventional yarn grippers in weft accumulators, in this study, a piezoelectric yarn gripper is used instead of conventional yarn grippers and the motion characteristics of its actuator are studied. This gripper uses a bimorph piezoelectric bending actuator with a low-cost, well integrated self-sensing method based on charge measurement. The modeling of the piezoelectric micromanipulator is based on the piezoelectric and Euler–Bernoulli beam equations. The static and dynamic characteristics of the piezoelectric actuator as well as the self-sensing capability were experimentally tested. The experimental results show that the maximum output displacement at the end of the piezoelectric actuator is 834 μm, and the maximum output force is 388 μN at 150 V driving voltage. The stability and consistency of its response are also very good, with a response speed of 24 ms. The self-sensing test of the output force also proved the feasibility of the self-sensing method used, with an error of 0.74%. The piezoelectric yarn gripper studied in this paper is promising for practical clamping applications.

In the process of printed matter quality inspection, the influence of lighting conditions leads to a large color difference between the captured image and the actual image. Therefore, this paper proposes an illumination correction model based on Remora Optimization Algorithm for Regularized Extreme Learning Machine (ROA-RELM). First, the algorithm extracts the statistical features of the printed matter image using the Grey-Edge framework. Next, the statistical features are used as inputs to the Regularized Extreme Learning Machine (RELM). Then, the remora algorithm (ROA) is used to optimize the weights w and bias b of the input layer of the RELM, which improves the prediction accuracy. At last, the image is corrected by color constancy algorithm based on the output. In order to verify the effectiveness of the algorithm proposed in this paper, the method of color difference detection is used in this paper to evaluate the effect of the illumination correction algorithm on the detection of the color quality of printed matter. The experimental results show that the algorithm mentioned in this paper can effectively improve the presentation of printed matter images, and the angle root-mean-square error is reduced by 11.04% compared with the control group’s illumination-corrected model.

Aiming at the problem of the malfunction of knitting machines caused by the unstable operation of the piezoelectric needle selector during the jacquard process, a state detection scheme for the piezoelectric needle selector that integrates the sensor and drive function co-located is proposed. The motion state of bimorph piezoelectric cantilever beam and its bi-directional piezoelectric effect in the jacquard process of piezoelectric needle selector are analyzed. Electrical and dynamic models were established for the electrical characteristics inside the bimorph piezoelectric cantilever beam and the dynamic characteristics of the cutter head and baffle of the needle selector. The signal detection circuit is designed to realize the real-time detection of the piezoelectric needle selector state by analyzing the time domain and frequency domain characteristics of the electrical signal. The results show that in the normal working state of the piezoelectric needle selector, the internal electric signal of the piezoelectric driver has two characteristic frequencies, which are between 155 and 180 and between 1630 and 1670 Hz, and the time for the piece to swing to the limit position is relatively long under abnormal working conditions.

Solid ink density is an important control parameter in the manufacturing process of offset prints—the size of which has a significant impact on the color performance of the prints—in which the determination of the optimal solid ink density is critical for the pre-press phase of industrial production. Compared with the traditional method of determining the optimal solid ink density, the current printing equipment used to determine the optimal solid ink density will be faster at improving industrial production efficiency and product quality. To improve the efficiency of determining the optimal solid ink density, the Random Forest algorithm was applied for the first time to the prediction task of solid ink density in offset printing. An optimal solid ink density prediction model for lithographic offset printing is established, and the L*a*b* colorimetric values of CMY three-color prints are used as inputs for training through hyperparameter optimization of the model. The experimental data show that the relevant evaluation metrics MAE, RMSE, MSE, and R² of the model are within the reliable range. A comparison between the proposed prediction model and several mainstream machine-learning algorithms indicates that the Random Forest model performs superiorly in both the coefficient of determination (R²) and the mean squared error (MSE). Specifically, the Random Forest model achieved an R² value of 0.969, reflecting improvements of 27.5%, 1.89%, 3.8%, and 34.02% compared to artificial neural network, gradient boosting, polynomial regression, and support vector regression models, respectively. In terms of MSE, the model reduced prediction error by 87.1%, 36.2%, 55.4%, and 89.2%, respectively, when compared with the same models. This approach has proven to provide both theoretical support and a practical pathway for enhancing the level of intelligence in pre-press process control, demonstrating significant practical application value.