In this work, different ring VCO topologies and architectures are designed to improve the performance of the conventional VCO structure. A single-ended ring VCO is designed and implemented at different control voltages. The output frequency range observed is between 3.27 and 12.57 GHz with the control voltage ranging from 1 V to 0.5 V. The minimum delay measured is 17.8 picoseconds. The other architecture involves the reverse substrate-bias (SB) technique and differential structure for further improvement of the performance parameters of the VCO. All the topologies are designed in Cadence Virtuoso with gpdk 90 nm technology. The differential structure and reverse SB structure result in frequency ranges of 17.405 GHz to 10.982 GHz and 11.87 GHz to 13.77 GHz, respectively. The results demonstrate a minimum delay, and the power consumptions are 8.1 picoseconds and 62.42 µW for the differential configuration and 8.27 picoseconds and 32.96 µW for the reverse-substrate bias technique, respectively. Overall, the voltage-controlled ring oscillator with reverse substrate bias is most suitable for delay reduction.

The failure of a system depends on many parameters, such as complexity, time, design, reliability of components, and operating conditions. If failure depends on the stress of a component, such reliability models are called stress dependent models. There are many types of stresses that occur in the body, like tensile, compressive, shear, and bending. Shear stress develops in a body when a pair of opposite forces act across the section tangentially. A reliability analysis over the I-section of a beam due to a uniformly distributed load has been conducted by finding the shear stress in the web and flange of the I-section. It is obtained that the maximum stress occurred at the neutral axis of the section and zero stress at the top of the section. In this article, the failure rate is considered to follow a Weibull distribution. The reliability is derived for constant, linear, and non-linear failure rates and is computed for various parameters. It is observed from the computations that reliability decreases as the load and overall depth of the section increase. It also increases as the thickness and depth of the web increase.

Three-wheeler electric vehicles are receiving exclusive attention in developing countries like India. For short-distance transportation of public and cargo facilities, these vehicles are extensibly used. These vehicles have the potential to decarbonize the road transportation sector. Additionally, these vehicles are concise, lightweight, and green vehicles compared to internal combustion engine technology-based three-wheeler vehicles. The heart of the three-wheeler electric vehicle is the propulsion system, which supplies the required traction power to move the vehicle. In this study, an accurate estimation of the electric propulsion system is presented as per the existing Indian driving cycle for three-wheeler vehicles. Technological evaluation of appropriate electric propulsion system for three-wheeler electric vehicles among several motors according to their operating performance is carried out. Furthermore, the operating power, torque, and speed of the electric propulsion system are computed from the longitudinal vehicle dynamic model based on the existing Indian driving cycle. Simulations are performed in MATLAB^® programming environment to compute operating parameters of an electric propulsion system. Finally, a comparison is conducted between the present study model and literature model of an electric propulsion system for three-wheeler battery vehicles.

The development of complex systems such as industrial process plants is accompanied by a continuous improvement of industrial safety. This remains an important element such as production, in a world where accidents continue to cause a high number of fatalities and severe economic and material losses. In addition, these losses cannot avoid significant damages to the environment that have a negative effect on the present and future of society. A better way to deal with these complex systems is to use risk management, which is a necessary priority for our society and our companies today. It is essential to develop or integrate quantitative approaches in risk assessments to evaluate the safety of complex processes. The present work proposes a comprehensive risk assessment approach based on a bow tie diagram mapped to a Bayesian network, with the combination of a risk matrix. In this way, we firstly define the worst-case scenario by hazard analysis and then use a bow tie diagram to understand the flow of cause/effect relation between system components. This allows us to model the accidental scenario and then construct a Bayesian network. Secondly, a transformation operator is used to calculate the occurrence frequency of unwanted failures, which leads to the activation of various layers of protection within the system. Finally, a risk matrix is used to evaluate the residual risk with the help of a probability-severity ranking criterion. This proposed methodology has been applied to a gas treatment plant system based on risk management.

In order to realize the intelligent evaluation capability of the quality uniformity of the subgrade compaction construction, a method for evaluating the quality of the subgrade compaction construction based on the multi-distribution sensing information feature extraction is proposed. A multi-dimensional sensor is adopted for detecting the quality of the roadbed compaction construction quality. The quality uniformity distribution identification model of the roadbed compaction construction is constructed. The maximum likelihood estimation method is adopted to perform the position calibration, and the statistical analysis of the quality uniformity of the subgrade compaction construction. By using the multi-resolution feature extraction and the large data mining method, the linear fitting of the statistical characteristics of the quality uniformity of the subgrade compaction construction is obtained, and the quality uniformity evaluation of the subgrade compaction construction is determined according to the fitting result. The optimal design of the automatic monitoring system for the quality of the subgrade compaction construction is carried out under the environment of the ARM embedded microprocessor. The test results show that the accuracy of the quality uniformity of the subgrade compaction construction is high, and the real-time positioning and detection performance of the quality uniformity of the subgrade compaction construction is good.

In view of the fact that there is no intelligent monitoring device in the existing sewage treatment process, this paper proposes using Siemens S7-300 series PLC as the core controller of the sewage control system. This paper also designs the PLC multi-computer communication based on the Profibus communication protocol, and the field bus sewage treatment control system based on the communication between the main controller and the power meter based on the Modbus protocol. Finally, a sewage treatment control system based on fieldbus is built, and the designed system is applied to the control process of sewage treatment in the CASS process, which greatly reduces the labor intensity of workers and the cost of sewage treatment.

In this paper, a sensor array using small ECM sensors is proposed to construct a lightweight heart sound pickup device, and a data processing unit combining FPGA and MCU is established to interface with the sensor array and perform multiple sensor data processing jobs. By studying this sensor array and data processing unit, a possible method of designing a small-size, low-cost heart sound recorder is presented. The prototype device combining the sensor array and data processing unit running with the array data fusion algorithm gives the following result: the background noise level of heart sounds processed by the data processing unit is 2 dB lower than that of any single sensor output in the sensor array. Also, a comparison between the heart sounds recorded by the prototype device and those recorded by a dedicated heart sound sensor is made, showing that the prototype device is capable of recording heart sounds with more details. The sensor array and data processing unit is suitable for achieving compact and lightweight heart sound acquisition.

To restore the degraded basketball video image, improve the adaptive tracking fusion ability of the image and improve the video information tracking ability of the degraded basketball video, we can accurately analyze the video image tracking curve of the degraded basketball video, so as to guide the basketball training. In this paper, a quality evaluation method of degraded basketball video image restoration based on classified learning and degraded basketball video information feature fitting capture is proposed. Firstly, the degraded basketball video image sequence is collected. The wavelet scale feature decomposition method is used to reduce the noise of the image, and then the gray pixel value feature point fitting capture of the degraded basketball video image sequence is carried out to realize the image quantitative analysis of basketball shooting trajectory. Finally, the simulation results show that the proposed method has high fitting degree and high precision of feature point extraction, which is of positive significance guiding basketball training.

In order to improve the privacy protection ability of the data information of the characteristic tourism virtual experience system in Changbai Mountain, the data information protection quality management model of the characteristic tourism virtual experience system based on the linear encryption and the statistics mapping is put forward. The large data distribution structure model of the data information of the characteristic tourism virtual experience system of the Changbai mountain is constructed. The fuzzy correlation feature quantity of the data information of the characteristic tourism virtual experience system of the Changbai Mountain is extracted. Lastly, the extracted Changbai Mountain characteristic tourism virtual experience system data information correlation feature quantity is encoded and rearranged by adopting a line space reconstruction method. By adopting the non-linear vector quantization coding method, the homomorphic fusion encryption of the data information of the characteristic tourism virtual experience system in Changbai Mountain is carried out, the random linear encryption key and the decryption key are designed, and the encryption transmission and the information protection of the data information of the characteristic tourism virtual experience system of the Changbai Mountain are realized. The simulation ends in various results: the data information protection quality management performance of the characteristic tourism virtual experience system of Changbai Mountain is good, the data information protection quality management capability is strong, the anti-attack capability is good, and the characteristic tourism virtual data encryption and protection control capability of the Changbai Mountain is good.

In order to improve the recognition ability of the detail action of a multi-frame sports image, a local fusion quality evaluation method based on gradient domain is proposed. The sampling model of visual feature information of multi-frame sports image is established, the gradient feature decomposition and action feature reconstruction of multi-frame sports image are carried out by using block feature fusion matching method, and the key motion feature point matching model of multi-frame sports image is established. The local fuzzy feature detection of multi-frame sports images is carried out by template automatic matching and gradient domain feature decomposition. The block identification model of local fuzzy features of multi-frame sports images is established. The adaptive image information enhancement technology is used for multi-scale feature decomposition and information enhancement processing. The feature set of the key action of the local fuzzy region of the multi-frame sports image is extracted. According to the feature distribution of the block area of the key action, the fusion quality evaluation of the multi-frame sports image is realized. The simulation results show that the method has high accuracy in evaluating the fusion quality of multi-frame sports images, has a strong ability to distinguish multi-frame sports movements, and has guiding value in the guidance and judgment of multi-frame sports training.

In order to improve the process control ability of enterprise human resource management, an adaptive monitoring method of enterprise human resource management quality based on big data analysis is proposed. The constraint parameter model of enterprise human resource management quality adaptive monitoring is constructed, the grey relational analysis and feature extraction method are used to analyze the quality performance of enterprise human resource management process, the adaptive monitoring model of enterprise human resource management quality is established, and the process optimization and quality control of enterprise human resource management are carried out by using adaptive game and equilibrium optimization methods. The statistical feature analysis model of adaptive monitoring of enterprise human resource management quality is established, and the big data analysis and optimization control of enterprise human resource management process quality is realized by using fuzzy information fusion and adaptive optimization method. The simulation results show that the adaptive monitoring of enterprise human resource management quality is better, the stability is strong, and the adaptive monitoring ability of enterprise human resource management quality is improved.

This paper proposes a method for extracting the specific wavelengths of milk fat particles in a liquid absorbing medium by measuring particle size with the method of total light scattering. After considering the absorption of incident light by the liquid medium, this paper conducts simulation analysis of apparent optical parameters. At the same time, this paper simulates the extinction spectrum of common R-R distributed visible light using the apparent extinction coefficient and proposes a method for extracting specific wavelengths based on the immune particle swarm optimization algorithm (IA-PSO). According to this method, the wavelength point with the smallest root mean square error between the simulated extinction value and the extinction value is selected as the measuring wavelength, which can effectively eliminate the subjective effect in the process of optimal wavelength selection. To verify the validity of the method proposed by this paper, inversion calculation of the unimodal R-R distributed spectra and the bimodal R-R distributed spectra is performed respectively in the independent mode using the PT algorithm with simulated data. In the non-independent mode, the inversion of particle size distribution is performed based on the genetic optimization algorithm. The simulation experiment results indicate that the proposed method for extracting the specific wavelengths of milk fat particles in the liquid absorbing medium can effectively improve the accuracy and stability of the inversion results and reduce measuring errors.

In this paper, an improved RGB-D SLAM scheme is proposed to solve the problems of great tracking error, long time-consuming in front-end, and high pressure of algorithm in back-end. The realization scheme is mainly aimed at improving the visual odometry. First, the ORB extraction algorithm is used to extract the feature points of the current frame and calculate the descriptors. Then, the current frame is matched with the local map by descriptors, and the matched feature points are added to the local map. Finally, the camera pose is calculated by the PnP algorithm. In order to ensure the tracking accuracy, a triangulation algorithm is proposed to optimize the depth value of map points in the local map. A series of fr1 datasets in the tum database are tested. The experimental results show that the real-time performance is guaranteed, and the RMSE (root mean square error) in the tracking process is reduced by 9% on average. Moreover, the image indexes of the point cloud image generated in the tracking process are also significantly improved.

In view of the problem that the accuracy of the LANDMARC indoor positioning algorithm based on radio frequency identification (RFID) is subject to the influence of adjacent reference tags, this paper proposes an improved LANDMARC positioning algorithm. According to the distance between the nearest reference tag and the reader, as well as the received signal strength indicator (RSSI), the improved algorithm uses Newton interpolation to obtain the distance between the tag to be measured and the reader and calculates the coordinates of the tag to be measured using the theorem on trilateral generating functions. The results of MATLAB simulation show that the improved LANDMARC positioning algorithm lowers the positioning error of the system, has higher positioning accuracy, and minimizes the complexity of the algorithm.

The biggest drawback of the K-means algorithm is that the number of clusters must be specified before use, and the central point is randomly initialized. To make up for this shortcoming, this paper proposes an improved algorithm of K-means called the linear fitting density peak K-means algorithm (LDKM), which realizes the automatic initialization of K-means and improves the accuracy of the algorithm. The LDKM algorithm is applied to the field of image segmentation and compared with the K-means algorithm, and the experimental results have clear outline and less noise. The LDKM algorithm is applied to the classification and recognition of white blood cells, and the experimental results show that the LDKM algorithm can extract white blood cells completely and obtain pure results.

The hybrid electric vehicle, a combination of the conventional fuel vehicle and the pure electric vehicle, is the main development direction in the future due to its unique dynamic structure. The design of a well-performing control strategy is of positive practical significance to the improvement of both the dynamic performance and safety of the vehicle. Based on an analysis of the dynamic system structure and working mode of the hybrid electric vehicle, this paper designs a multi-mode control strategy including start/reverse mode, drive mode, and braking/sliding mode according to the control requirements of hybrid electric vehicles, while optimizing the threshold strategy of mode switching and avoiding frequent switching between modes. According to the dynamic system state and the driver’s driving habits, this paper formulates the switching rules for working states of dynamic systems based on torque and vehicle speed and optimizes the power distribution strategy of the engine, motor, and generator. In addition, this paper establishes a control strategy model in Simulink for simulation, and the simulation results indicate that the control strategy can implement effective control of the vehicle’s dynamic system.

In this paper, the threshold function denoising algorithm and mathematical morphology are combined and applied to image edge detection. Firstly, we construct a dyadic wavelet with non-orthogonality, symmetry, limited spectrum, and smoothness almost everywhere. Then, the properties of the dyadic wavelet are discussed, and the analytic expression of the reproducing kernel function in the image space of dyadic wavelet transform is given. Moreover, the dyadic wavelet is used to construct a new threshold function for image denoising, and the new threshold function has a clear effect on image denoising. Finally, we present an improved morphological edge detection algorithm, which is applied to extract the edges of images after threshold denoising. Thus, we can obtain a new edge detection algorithm that combines the threshold function denoising algorithm and morphological edge extraction algorithm. The simulation results show that the edges detected by the new algorithm are clearer and contain less noise, and the continuity and accuracy are also improved.

Aiming at the shortcomings of longtime consumption and complex algorithm in multi-threshold image segmentation, the maximum inter-class variance method and the maximum entropy threshold method are described as segmentation standards. Secondly, from the point of view of initialization and falling into local optimization of particle swarm optimization algorithm, Kent mapping is introduced, and nonlinear digressive extremum is improved. Finally, the improved particle swarm optimization algorithm is applied to multi-threshold image segmentation. Simulation results show that the algorithm has a good segmentation effect and the time consumption can be effectively reduced.

In the context of big data, how Chinese Internet companies make strategic choices has gained wide interest, but few people have explored the formation basis and change rule of strategic decision in this kind of enterprises in the development process. The development of non-market strategies by enterprises is fundamentally influenced by strategic logic, which is objectively restricted by market elements and non-market factors. Based on the analysis framework of strategic analysis logic, this paper makes an in-depth analysis of the development of Internet enterprise strategic decision-making and takes Baidu company as the research object. It also expounds its strategic decision basis and development law in three important development stages: the decisive role of strategic logic in the process of strategic selection and evolution, as well as the auxiliary role of market elements and non-market factors. It provides a new evidence and theoretical contribution to the research of enterprise strategic logic development.