Bertramjohannessen9707

4 mm and 0.6 mm, which is almost the same as the offline hand-eye calibration accuracy. selleck The method in this paper utilizes the advantages of the ChArUco board to realize online hand-eye calibration, which improves the flexibility and robustness of hand-eye calibration.This paper presents the implementation of a measurement system that uses a four microphone array and a data-driven algorithm to estimate depth of cut during end milling operations. The audible range acoustic emission signals captured with the microphones are combined using a spectral subtraction and a blind source separation algorithm to reduce the impact of noise and reverberation. Afterwards, a set of features are extracted from these signals which are finally fed into a nonlinear regression algorithm assisted by machine learning techniques for the contactless monitoring of the milling process. The main advantages of this algorithm lie in relatively simple implementation and good accuracy in its results, which reduce the variance of the current noncontact monitoring systems. To validate this method, the results have been compared with the values obtained with a precision dynamometer and a geometric model algorithm obtaining a mean error of 1% while maintaining an STD below 0.2 mm.The roll-bearing-bearing housing (RBBH) system is one of the most common kernel structures used to determine strip mill stability and product surface quality in modern metallurgical machinery. To better understand dynamic characteristics of the RBBH system, this paper provides a nonlinear dynamic model and designs an engineering test platform on the RBBH system in the whole rolling process. First, a nonlinear dynamic model of the RBBH system supported by four-row rolling bearings under high speed and heavy load is established. Then, the method of combining Riccati transfer matrix and Newmark-β numerical integration is employed to solve nonlinear dynamic equations. After that, the engineering test platform is designed and assembled to capture and analyze the vibration signals of weathering steel (SPA-H) with finished thicknesses of 1.6 and 3.2 mm. Finally, the dynamic characteristics of the RBBH system are studied with the method of the dynamic model and vibration data fusion. The results show that the SPA-H with a finished thickness of 1.6 mm is rolled, the RBBH system fluctuates violently in both horizontal and vertical directions, and numerical results are highly consistent with experimental results in acceleration response, velocity response, and displacement response. In addition, the dynamic performance parameters of the four-row rolling bearing will also fluctuate greatly. Finally, there is significant interest to gain the benefits for the RBBH system design and mill stable rolling purposes.With smart electronic devices delving deeper into our everyday lives, predictive maintenance solutions are gaining more traction in the electronic manufacturing industry. It is imperative for the manufacturers to identify potential failures and predict the system/device's remaining useful life (RUL). Although data-driven models are commonly used for prognostic applications, they are limited by the necessity of large training datasets and also the optimization algorithms used in such methods run into local minima problems. In order to overcome these drawbacks, we train a Neural Network with Bayesian inference. In this work, we use Neural Networks (NN) as the prediction model and an adaptive Bayesian learning approach to estimate the RUL of electronic devices. The proposed prognostic approach functions in two stages-weight regularization using adaptive Bayesian learning and prognosis using NN. A Bayesian framework (particle filter algorithm) is adopted in the first stage to estimate the network parameters (weights and bias) using the NN prediction model as the state transition function. However, using a higher number of hidden neurons in the NN prediction model leads to particle weight decay in the Bayesian framework. To overcome the weight decay issues, we propose particle roughening as a weight regularization method in the Bayesian framework wherein a small Gaussian jitter is added to the decaying particles. Additionally, weight regularization was also performed by adopting conventional resampling strategies to evaluate the efficiency and robustness of the proposed approach and to reduce optimization problems commonly encountered in NN models. In the second stage, the estimated distributions of network parameters were fed into the NN prediction model to predict the RUL of the device. The lithium-ion battery capacity degradation data (CALCE/NASA) were used to test the proposed method, and RMSE values and execution time were used as metrics to evaluate the performance.Analysing the dynamics in social interactions in indoor spaces entails evaluating spatial-temporal variables from the event, such as location and time. Additionally, social interactions include invisible spaces that we unconsciously acknowledge due to social constraints, e.g., space between people having a conversation with each other. Nevertheless, current sensor arrays focus on detecting the physically occupied spaces from social interactions, i.e., areas inhabited by physically measurable objects. Our goal is to detect the socially occupied spaces, i.e., spaces not physically occupied by subjects and objects but inhabited by the interaction they sustain. We evaluate the social representation of the space structure between two or more active participants, so-called F-Formation for small gatherings. We propose calculating body orientation and location from skeleton joint data sets by integrating depth cameras. The body orientation is derived by integrating the shoulders and spine joint data with head/face rotation data and spatial-temporal information from trajectories. From the physically occupied measurements, we can detect socially occupied spaces. In our user study implementing the system, we compared the capabilities and skeleton tracking datasets from three depth camera sensors, the Kinect v2, Azure Kinect, and Zed 2i. We collected 32 walking patterns for individual and dyad configurations and evaluated the system's accuracy regarding the intended and socially accepted orientations. Experimental results show accuracy above 90% for the Kinect v2, 96% for the Azure Kinect, and 89% for the Zed 2i for assessing socially relevant body orientation. Our algorithm contributes to the anonymous and automated assessment of socially occupied spaces. The depth sensor system is promising in detecting more complex social structures. These findings impact research areas that study group interactions within complex indoor settings.The linearity of active mixers is usually determined by the input transistors, and many works have been proposed to improve it by modified input stages at the cost of a more complex structure or more power consumption. A new linearization method of active mixers is proposed in this paper; the input 1 dB compression point (IP1dB) and output 1 dB compression point (OP1dB) are greatly improved by exploiting the "reverse uplift" phenomenon. Compared with other linearization methods, the proposed one is simpler, more efficient, and sacrifices less conversion gain. Using this method, an ultra-high-linearity double-balanced down-conversion mixer with wide IF bandwidth is designed and fabricated in a 130 nm SiGe BiCMOS process. The proposed mixer includes a Gilbert-cell, a pair of phase-adjusting inductors, and a Marchand-balun-based output network. Under a 1.6 V supply voltage, the measurement results show that the mixer exhibits an excellent IP1dB of +7.2~+10.1 dBm, an average OP1dB of +5.4 dBm, which is the state-of-the-art linearity performance in mixers under a silicon-based process, whether active or passive. Moreover, a wide IF bandwidth of 8 GHz from 3 GHz to 11 GHz was achieved. The circuit consumes 19.8 mW and occupies 0.48 mm2, including all pads. The use of the "reverse uplift" allows us to implement high-linearity circuits more efficiently, which is helpful for the design of 5G high-speed communication transceivers.Single-pixel imaging (SPI) has attracted widespread attention because it generally uses a non-pixelated photodetector and a digital micromirror device (DMD) to acquire the object image. Since the modulated patterns seen from two reflection directions of the DMD are naturally complementary, one can apply complementary balanced measurements to greatly improve the measurement signal-to-noise ratio and reconstruction quality. However, the balance between two reflection arms significantly determines the quality of differential measurements. In this work, we propose and demonstrate a simple secondary complementary balancing mechanism to minimize the impact of the imbalance on the imaging system. In our SPI setup, we used a silicon free-space balanced amplified photodetector with 5 mm active diameter which could directly output the difference between two optical input signals in two reflection arms. Both simulation and experimental results have demonstrated that the use of secondary complementary balancing can result in a better cancellation of direct current components of measurements, and can acquire an image quality slightly better than that of single-arm single-pixel complementary measurement scheme (which is free from the trouble of optical imbalance) and over 20 times better than that of double-arm dual-pixel complementary measurement scheme under optical imbalance conditions.With the fast development of giant LEO constellations, the effective spectrum utilization has been regarded as one of the key orientations for satellite communications. This paper focuses on improving the spectrum utilization efficiency of satellite communications by proposing a non-continuous orthogonal frequency division multiplexing (NC-OFDM) method. Based on the models of NC-OFDM system, we first propose a sub-carrier allocation method by using spectrum sensing to efficiently perceive and utilize the spectrum holes in the satellite communication system. Then, a hybrid genetic particle swarm optimization method is adopted to allocate the channel resources effectively. Finally, simulation results verify that the proposed algorithm can significantly improve the spectrum efficiency of satellite communications.Reducing pollutant detection time based on a reasonable sensor combination is desirable. Clean drinking water is essential to life. However, the water supply network (WSN) is a vulnerable target for accidental or intentional contamination due to its extensive geographic coverage, multiple points of access, backflow, infrastructure aging, and designed sabotage. Contaminants entering WSN are one of the most dangerous events that may cause sickness or even death among people. Using sensors to monitor the water quality in real time is one of the most effective ways to minimize negative consequences on public health. However, it is a challenge to deploy a limited number of sensors in a large-scale WSN. In this study, the sensor placement problem (SPP) is modeled as a sequential decision optimization problem, then an evolutionary reinforcement learning (ERL) algorithm based on domain knowledge is proposed to solve SPP. Extensive experiments have been conducted and the results show that our proposed algorithm outperforms meta-heuristic algorithms and deep reinforcement learning (DRL).