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Vibration affects the function of precision instruments and equipment, causing major structural deformation and damage. Strong vibration and noise cause serious public hazards. Therefore, vibration is considered to be a negative factor. There are many methods for designing vibration controllers, such as machine learning algorithms and artificial intelligence algorithms. In order to reduce the harm of vibration, this paper proposes an improved Youla parameterized adaptive controller to suppress fixed or time-varying sinusoidal disturbances. The algorithm uses fuzzy control to adjust the forgetting factor, so that the system quickly reaches a steady-state and keeps the steady-state error unchanged. The paper illustrates that the improved Youla parameterized adaptive controller can suppress the disturbance. The convergence speed of this algorithm is better than the original Youla parameterized adaptive controller, and the steady-state error is basically unchanged.

Precise positioning and vibration isolation are the key technologies in modern precision engineering, such as the precise assembly manipulators and UV lithography, where isolating precision components from the environmental disturbances during positioning is required. In this paper, a novel magnetic levitation gravity compensator employed in a precise assembly system is proposed, which can carry out high-precise positioning in ±5 mm stroke and superior vibration isolation performance attributed to its low levitation stiffness (72.11 N/m) and natural frequency (0.698 Hz). In order to design the gravity compensator with ultra-low stiffness and natural frequency, the analytical model of levitation force is deduced by equivalent surface current model and validated by FEM model. Moreover, a hybrid optimization strategy is utilized to obtain the optimal dimensional parameters of gravity compensator.

The applications of parallel robots in various fields are increasing. In this paper, a research on the design optimization of a 6-PSS parallel manipulator for motion simulating is presented. The optimization problem of maximizing the workspace is formulated according to geometric and kinematic analysis. In particular, the workspace is a totally flexible workspace with 6 degrees-of-freedom (DOF). To maximize the workspace of the 6-PSS parallel robot, we investigated the differential evolution algorithm (DE), and the genetic algorithm (GA) is also adopted as a comparison. Computational results show DE algorithm outperforms GA algorithm at about 48.3% in optimal solution providing and 9.3% in time consuming.

Based on the sound card as the signal source and acquisition module, the signal source is guided to each link of the line sequence to be determined through the multi-channel relay, The sound card is used to collect the signal and analyze the frequency and amplitude to determine the continuity and sequence of the link, It includes the setting of signal output type, output frequency, sampling length, sampling number and channel number, and the realization of corresponding control program, The software of the system is written with LabVIEW and the hardware transfer circuit, And serial port signal switching module and other hardware, can display the signal source waveform in real time, display the collected signal waveform and the determined line sequence and conduction results. The results show that: The system design is a consistent, stable, fast and high accuracy solution for line sequence or continuity test requirements including capacitance and capacitance like devices, The system has good expansibility, especially for the number of lines between a bundle, the more efficient the system can be reflected. After actual production verification, the overall accuracy of the system is more than 99.9%.

Pedestrian Attribute Recognition (PAR) has received extensive attention during the past few years. With the advances of deep convolutional neural networks (CNNs), the performance of PAR has been significantly improved. Existing methods tend to acquire attribute-specific features by designing various complex network structures with additional modules. Such additional modules, however, dramatically increase the number of network parameters. Meanwhile, the problems of class imbalance and hard attribute retrieving remain underestimated in PAR. In this paper, we explore the optimization mechanism of the training processing to account for these problems and propose a new loss function called Multi-label Contrastive Focal Loss (MCFL). This proposed MCFL emphasizes the hard and minority attributes by using a separated re-weighting mechanism for different positive and negative classes to alleviate the impact of the imbalance. MCFL is also able to enlarge the gaps between the intra-class of multi-label attributes, to force CNNs to extract more subtle discriminative features. We evaluate the proposed MCFL on three large public pedestrian datasets, including RAP, PA-100K, and PETA. The experimental results indicate that the proposed MCFL with the ResNet-50 backbone is able to outperform other state-of-the-art approaches in term of mean accuracy.

Compiling a software package multiple times may result in different artifacts. This problem is called unreproducible builds, which affect the credibility of the software. Researchers mainly focus on how to automatically locate the causes for unre-producible builds. However, automatic fixing is rarely studied. In this paper, we propose ConstBin, an automated tool that helps to generate consistent artifacts. It captures unreproducible commands in a build process and automatically replace them with their fixing operations based on an extensible rules set. As far as we know, ConstBin is the first tool that fixes inconsistencies during build processes. We have evaluated this tool on 348 open source packages whose original builds are unreproducible. The results of the evaluation show that 83.91% of these builds have successfully generated consistent artifacts with the application of ConstBin.

With the fast development of human-machine interface technology, emotion recognition has attracted more and more attentions in recent years. Compared to other physiological experimental signals frequently used in emotion recognition, EEG signals are easy to record but not easy to disguise. However, because of high dimensionality of EEG data and the diversity of human emotions, feature extraction and classification of EEG signals are still difficult. In this paper, we propose deep forest with multi-scale window (MSWDF) to identify EEG emotions. Deep Forest is an integrated method of decision trees. In the MSWDF, features can be extracted by multi-granularity scanning with multi-scale windows. Compared with deep neural network, the MSWDF not only has less parameters to adjust, but also can realize the classification of the dataset with small samples. In the MSWDF, raw EEG signals were firstly filtered and segmented into samples. Regarding EEG signals as multivariate time series, a new multi-granularity scanning strategy with variable windows is proposed to extract features from EEG samples. After classifying EEG features by the cascade forest, the recognition results are compared with these of Nearest Neighbor algorithm (KNN), Naive Bayes, Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM). We found that the average classification accuracy of three emotions reaches to 84.90%, which is better than those of five compared methods.

In the future, household electricity consumption is used as part of the smart grid. It is of great significance to study the optimal operation of household intelligent electrical equipment. Firstly, the mathematical model of typical household intelligent electrical equipment in the future is established, and then based on the time-of-use electricity price, an optimized operation model of intelligent electrical equipment in the future home with the aim of economic and user comfort is proposed, which is convenient for users to formulate a power consumption plan that meets their own needs. Finally, taking a typical home user as an example, the genetic algorithm is used to optimize the operation of the home intelligent electrical equipment by establishing a typical daily simulation scenario. The simulation results show the effectiveness of the established optimization model.

Two vane-loaded three-beam folded waveguide structures, the symmetrical vane-loaded structure and the staggered vane-loaded structure, are proposed in this paper. Simulation results show that, for 220GHz traveling wave tube (TWT), the stagger vane-loaded structure has better cold characteristics and beam-wave interaction characteristics than those of the symmetrical vane-loaded structure.

Paper firstly defined the business power and right and the structure, pointed out that the power to enterprises means stakeholders to use of valuable resources to place a influence to a business organization structure and running mechanism and the capability arising form above, at the same time, pointed out the power structure for enterprises is the configuration and relationship in the corporate organization of the power of stakeholders. The right of enterprise is the exercise of power stakeholders or legal entity according to related law and policy or system and benefit enjoyed (opposite to obligation). The right structure of business organization is the configuration and relationship among rights of investors, operators and juridical person, staff and customers, suppliers rights and the government's rights. Based on the configuration condition of right and power both in classical and modern enterprises, analyzed that it was a game process between right and power in the corporation, pointed out following conclusions, such as a right relationship relies on the power relationship, there is a molding effect the power on the right, also a trend from power relationship to right relationship, power deciding the rights in the corporation, the right decided formal power, and power to protect and get the right, and right relying on the power.

The rotation mismatch between the hobbing cutter and rotary workbench in CNC gear hobbing machine will produce additional vibration during the process of gearing, thus reducing the machining precision. To achieve a better speed match, this paper proposes a synchronized control scheme based on cross-coupled control with proportional-integral (PI) compensator. Permanent-magnet synchronous motors (PMSM) in the servo system are controlled by vector control, which results in the simplification of their dynamic equations. The whole system with proposed control scheme is deduced mathematically. The simulation verifies the validity of the proposed control scheme.

With the development of nano material industry, more and more attention has been paid on the biological effect of nano materials. However, little research focused on the effect of nano TiO2 on protein level. In this study, we detected the toxic effect of nano TiO2 (50~60 nm) on DNA and functional proteinase. The oxidative damage of nano TiO2 on DNA was detected by gel electrophoresis and HPLC. Anson method was used to study the inhibition of nano TiO2 on pepsin, and the mechanism of the reaction was further researched by the method of UV-Vis scan and TEM. The results showed that the ratio of super-coiled DNA decreased significantly both under UV radiation and visible light. Levels of 8-OHdG generated from DNA increased from 27.66 8- OHdG/104 dG to 296.69 8-OHdG/104 dG after treated with nano TiO2. After water incubated with nano TiO2 at 37degC, the activity of pepsin decreased with the increase of the concentration of particels, UV-Vis scan and TEM detection proved the binding effect of nano TiO2 on pepsin, which indicated potential usage for oral drug delivery of nano TiO2.

In order to estimate the quality parameters in industrial processes, taking actual dynamic transition characteristics into account, a dynamic soft sensor modeling approach based on multiple least squares support vector machines (LSSVM) is presented. The input variables acquired in transition period are segmented according to their acquisition time point. Then LSSVMs are employed to map the relations of different time series of input variables to output variables. Moreover, a synthetical LSSVM is delivered to embody the dynamic characteristic of all the sub-networks to the model. A simulation example is put forward at last. The result shows that proposed method has simple dynamic structure and clear physical meanings, which features in better estimation precision and robustness than static soft sensors.