Modern Physics Letters B

In the scaling down of electronic devices, functional oxides with strongly correlated electron system provide advantages to conventional semiconductors. We report the fabrication of the electric double layer transistor (EDLT) with the (La,Pr,Ca)MnO₃ (LPCMO) epitaxial nanowall wire (NW) channel whose width was less than 100 nm. The width controlled LPCMO NWs were fabricated using an original nanofabrication technique: 3D nanotemplate pulsed laser deposition. The LPCMO NW EDLT was produced by employing 80 nm width NWs as channel. The produced LPCMO NW EDLT showed low leakage current. The metal-insulator transition property was successfully modulated by gating effect on the LPCMO NW EDLT.

The joint denoising method using multi-scale wavelet based on threshold value judgment and recursive least-squares (RLS) were proposed to realize lithium battery film thickness measurement. Continuous thickness measurement of thin film coating can be done via threshold value judgment. Low-frequency noise component similar to characters of film thickness can be reduced via RLS denoising. Joint denoising can further improve measurement accuracy. Compared with multi-scale wavelet denoising, the method proposed can improve the measurement precision by 6% which is more suitable to the thickness measurement of film coating.

Based on the rebound voltage data which was measured under different temperatures for lithium ion battery, a residual capacity prediction model used adaptive neuro-fuzzy inference system (ANFIS) was established. The rebound voltage and temperature were the input of prediction system, and the output was residual capacity. A large number of experimental data were used to train and verify the model. The reliability and applicability of the model were proven by applying it to predict and verify the residual capacity of different battery packs.

It is known that redox reaction can take place among the solutions of potassium ferrocyanide (K₄[Fe(CN)₆]), glucose (C₆H${}_{12}$O₆) and glucose oxidase (Glucose Oxidase, GOD). In this work, the method of electrochemical biosensor detection based on screen printed electrode was used to observe the redox reaction among these solutions. The relationship between redox reaction and parameters was studied by examining the effects of concentration and scanning speed of three solutions.

The lithium ion battery is considered as the experimental object, and its discharge characteristics are studied. A model of the battery in different charge-states is established by a tool of neural network while battery’s rebound voltage, temperature and load are set as input parameters. The validity of the model is tested based on the experimental data. The accuracy, adaptability and stability of the SOC in this model is validated in a variety of the working conditions, and the accuracy of the model is demonstrated to be higher than 5%.

Ba${}_{2.96}$(PO₄)₂:0.04Eu${}^{3+}$ (S₁) and Ba${}_{2.92}$Na${}_{0.04}$(PO₄)₂:0.04Eu${}^{3+}$ (S₂) were prepared by solid state reaction. The phases and luminescent properties of the obtained phosphors were characterized. The results demonstrate that the phosphors particles emit an intensive red light under excitation at 395 nm. Both phosphors can be efficiently excited by ultraviolet and blue light, and the emission spectra mainly consist of two emission peaks at 591 nm and 611 nm. The crystallographic sites of Eu${}^{3+}$ ions in Ba₃(PO₄)₂ host were discussed on the base of emission spectra and its host crystal structure. By introducing the charge compensator Na${}^{+}$ into the S₁ phosphor, its emission intensity was enhanced, indicating that S₂ could serve as a promising red-emitting phosphor for UV LED applications.

Factors affecting the on-line detecting thickness of lithium battery pole by laser sensor are mainly the accuracy of the sensor, the inherent frequency and dynamic error of the C scanning mechanism. Improved highly redundant optimal atomic parameters for the thickness of lithium battery pole piece under different circumstances are obtained. The best atomic parameters are extracted in the training phase to construct the sub-optimal atomic function. The results show that, compared with wavelet algorithm and conventional sparse decomposition algorithm, the improved sparse decomposition algorithm has better denoising performance under the condition of lacking prior knowledge.

Municipal solid waste has increased with the development of urbanization which sets a high request to the stability of landfill. The strength of anti-seepage slurry plays a crucial role in the stabilization of landfill. The ratio of each component was changed to test the slurry strength of different ages. Results show that the slurry strength increased with the raise of cement paste because the strength was mainly determined by the skeleton structure. In contrast, the excessive bentonite reduced the strength of slurry, for the excessive bentonite may package the skeleton structure of cement-fly ash instead of filling in. The dosage of Na₂CO₃ and PVA increasing to 3.0 g also reduced the strength of the slurry, because the water absorbability and expansibility of bentonite were inhibited and the effect of reinforcement on cement-fly ash structure was weaken.

Zirconium oxynitride films have recently received much attention because their properties may gradually be changed by varying the concentration of N, O and Zr constituents during processing. Here, we reported zirconium oxynitride films prepared by RF reactive magnetron sputtering technique. The films were deposited by different targets: ZrO₂ and ZrN with the variation of nitrogen and oxygen flow rate. To explore whether the N content could be gradually incorporated into the films, two series of samples were prepared: one was prepared by the ZrO₂ target with the variation of the nitrogen flow rate; the other was prepared by the ZrN target with the variation of the oxygen flow rate. The composition and structure of the films were investigated by XPS, EDS and XRD techniques. XPS results show that when the ZrO₂ material was used as target the concentration of N was highly dependent on the flow rate of nitrogen, while the Zr concentration gradually decreased with the increase of N concentration; ZrO${}_x$N${}_y$ and ZrO were formed. However, for ZrN used as target, the fresh film of the sample comprised of O and Zr elements without N detected. When the N₂ was introduced as reactive gas with oxidized ZrN target, ZrO${}_x$N${}_y$ film with a certain content of N was formed.

Copper oxide is a semiconducting compound with a narrow band gap and is used for photoconductive and photothermal applications. Most of the synthesis methods for the preparation of copper oxide composite materials either are unsuitable for mass fabrication or inevitably introduce unwanted impurities. In this work, we report on the synthesis of copper oxide composite materials by the arc discharge method with a pure copper rod as the anode and graphite as the cathode. Ion beam analysis techniques, particle-induced X-ray emission and Rutherford backscattering spectrometry were used to probe the impurities in the copper oxide composites. Ion beam analysis results revealed copper and oxygen as constituent elements with no impurities. X-ray diffraction results discovered the presence of CuO, Cu₂O and Cu phases in the composite materials. The morphology of the as-synthesized copper oxide was studied by scanning electron microscopy. Results clearly demonstrated that spherical particles were obtained with an average diameter of 14 $\mu$m (range 2–85 $\mu$m), 35 $\mu$m (range 20–100 $\mu$m) and 50 $\mu$m (range 30–120 $\mu$m) for the arc current of 60 A, 80 A and 95 A, respectively. It was found that the morphology can be controlled by the arc discharge parameters, e.g. a lower arc discharge current contributed to a smaller particle size. This is because the electric arc current influences the nucleation and the growth of the spherical structures. Due to its simplicity of synthesis, the proposed arc discharge is a promising technique for the fabrication of copper oxide composite materials for optical and electrical applications.

Flexible solar cells have drawn wide attention because of their high photoelectric conversion efficiency, convenient preparation, excellent bendability and lower cost advantages. This paper introduces the effect of mesoporous layer on the morphology of CH₃NH₃PbI₃ films. The uniformity and optical transmittance of the different films were also studied in detail. By adjusting the ratio of TiO₂ and ZrO₂, mesoporous structure of CH₃NH₃PbI₃ perovskite solar cells were prepared by two-step spin coating. The fabricated films were investigated by XRD, SEM and spectrophotometer. The results indicate that perovskite layers have good surface morphology, density and coverage with TiO₂ and ZrO₂ composition ratio of 1:1. These well-structured thin films lay a good foundation for the preparation of high performance flexible perovskite solar cells.

Sm–Co nanoparticles have promising applications in both scientific and industrial fields. In this paper, Sm–Co single-crystal nanoparticles were prepared using respective metal salts and complexing agents, such as citric acid by Pechini-type sol–gel method. The mixture solution was heated to form a highly viscous gel, and then heated at different temperatures to achieve SmCo-oxide. And then Sm–Co particles were yielded by reductive annealing of the precursors. Phase analysis, microstructural investigation and magnetic properties were performed by X-ray diffraction (XRD), field emission scanning electron microscope (FESEM), Fourier transform infrared (FT-IR) and VSM. TGA trace analysis of the gel determined the heat treatment temperature. XRD results showed that SmCoO₃ and Co₃O₄ were obtained at 800${}^{\circ }$C, FT-IR spectrometry analysis also verified the results. In the calcium-thermal reduction process, Sm₂O₃ and Co were obtained after reductive annealing at 800${}^{\circ }$C, meanwhile Sm–Co nanoparticles were formed at 850${}^{\circ }$C. FESEM analysis revealed that the Sm–Co nanoparticles were composed in the form of spherical granules and exhibited well distribution with size in the range of 100 nm. The room temperature saturation magnetization and coercivity of the Sm–Co nanoparticles were 49.61 emu/g and 3.53 kOe, respectively.

NdFeB nanoparticles were synthesized by a sol–gel method. Firstly, NdFeB gel was prepared by using citric acid and glycol as the gel, and the corresponding salts of Nd, Fe and boric acid. The gel was subsequently annealed under a high temperature condition to obtain NdFeB-oxide phases. Then NdFeB nanoparticles were yielded by reductive annealing of precursors. Microstructure, phase composition and magnetic properties were characterized by X-ray diffraction (XRD), field emission scanning electron microscope (FESEM) and vibrating sample magnetometer (VSM), and also differential scanning calorimetry (DSC) analysis was performed to determine the annealing temperature. At 400${}^{\circ }$C, intermediate phases of Fe₃BO₅ and NdBO₃ were formed and stable up to 600${}^{\circ }$C; while at 800${}^{\circ }$C, they would reacted in presence of oxygen to form NdFeO₃ and B₂O₃. Nd₂O₃ and Fe₂O₃ were also formed at this temperature. SEM micrographs of reduced powders after washing process showed an average particle size below 100 nm in all the samples. VSM results showed the oxide powder formed at 800${}^{\circ }$C had a coercivity of 1.55 kOe and a saturation magnetization of 1.08 emu/g. The powder synthesized by reduction of oxide powders at 800${}^{\circ }$C had a coercivity of 1.02 kOe and a saturation magnetization of 14.17 emu/g.

In this work, we investigate the influences of Ba/Ti ratio and sintering conditions on the characteristics of positive temperature coefficient of resistance (PTCR) and electrical properties of Ba${}_m$(Ti${}_{1-x}$Nb${}_x$)O₃ (BTN) ceramics. The ceramics were fired at 1190${}^{\circ }$C for 0.5–6.0 h in a reducing atmosphere and then reoxidized at 600–650${}^{\circ }$C for 0–8 h. The Ba/Ti ratio $(m)$ affected the electrical properties and PTCR effect of the BTN specimens. The room-temperature (RT) resistance of the BTN samples initially decreased $(0.994<m<1.006)$ and then increased $(1.006<m<1.018)$ as the Ba/Ti ratio increased. Moreover, BTN ceramics exhibit a pronounced PTCR effect, with a resistance jump greater by 3.0 orders of magnitude and a low 0.1 $\mathrm{\Omega }$ RT resistance at a low reoxidation temperature of 600${}^{\circ }$C after sintering under a reducing atmosphere. Furthermore, the average sample grain size increased along with the Ba/Ti ratio. In addition, the influence of the sintering time and the reoxidation time on the electrical properties and the PTCR effect were also investigated.

The bulk fin field-effect transistor (FinFET) has been the primary semiconductor technology in nanotechnology. To protect low supply voltage circuits based on FinFET, trigger voltage $(V_{\mathrm{\text{tr}}})$ of the silicon controlled rectifier (SCR) which acts as electrostatic discharge (ESD) protection device should be lowered further. In this paper, in order to lower the $V_{\mathrm{\text{tr}}}$ an extra implant technique is proposed to form bridging well low trigger voltage FinFET SCR (FinFET BRLVTSCR). The experiments demonstrate that the trigger voltage can be lowered effectively. Moreover, the TCAD simulations bring an in-depth physical understanding of ESD current conduction and failure mechanism during ESD protection. Finally, the turn-on characteristic demonstrates proposed novel SCRs are fast and effective under TLP and very fast TLP (VFTLP) stress.

Azimuthal electromagnetic wave logging-while-drilling (LWD) technology can detect weak electromagnetic wave signal and realize real-time resistivity imaging. It has great values to reduce drilling cost and increase drilling rate. In this paper, self-adaptive hp finite element method (FEM) has been used to study the azimuthal resistivity LWD responses in different conditions. Numerical simulation results show that amplitude attenuation and phase shift of directional electromagnetic wave signals are closely related to induced magnetic field and azimuthal angle. The peak value and polarity of geological guidance signals can be used to distinguish reservoir interface and achieve real-time geosteering drilling. Numerical simulation results also show the accuracy of the self-adaptive hp FEM and provide physical interpretation of peak value and polarity of the geological guidance signals.

Molecular pumps have been widely used in the vacuum metallurgy, coating, semiconductor manufacturing and many other fields in which the high vacuum, ultra-clean environment is needed. The application of magnetic bearings can bring many advantages for molecular pump, such as eliminating the friction, decreasing the power loss, lowering the maintenance costs, and increasing the rotating speed and service life. Besides, the magnetic bearings can fundamentally solve the vacuum chamber pollution problem which is caused by the backflow of lubrication oil steam. The three-pole magnetic bearings are the simplest structure of radial magnetic bearings and can be driven by three-phase converter which has the advantages of low costs, small volume and low power loss. In this paper, the performance of the three-pole active magnetic bearing (AMB) and hybrid magnetic bearing (HMB) are compared based on radial force–current characteristics analysis. Firstly, the mathematical model of three-pole AMB and HMB is built by equivalent magnetic circuit model, and the radial force–current characteristics are analyzed. Then, simulation by the three-dimensional (3D) finite element method (FEM) is performed. Finally, the experiment is conducted. The FEM results are consistent with the analytical results, showing that the nonlinearity and coupling of three-pole HMB are lower than three-pole AMB. The reason of causing nonlinearity and coupling is also discussed.

Rapid capturing and isolation of magnetic nanoparticles is critical for microfluidic biodetection system. A numerical model based on multiphysics coupled-field finite-element method is developed for evaluating the capture and segregation of magnetic nanoparticles in microfluidic channel. The model consists of microfluidic channels surrounded by four electromagnets changed periodically. The trajectories and trapping efficiencies are numerically analyzed for multiple magnetic nanoparticles released in microfluidic channel. Simulation results showed that magnetic pole current, inlet velocity and diameter of magnetic nanoparticles have an impact on the capture efficiency of magnetic nanoparticle. Thus, the model with the optimal parameters can reasonably predict and evaluate completely trapping of 180 nm magnetic nanoparticles in the simulation. The numerical analysis will encouraging for the design and development of new microfluidic bioseparation and detection microsystems.

A new method for colorimetric detection of solution concentration is presented. A paper-based microfluidic device was designed and made using printed circuit board technology. Detection zone patterns were designed and transferred to a printed circuit board by hollowing technology. The printed circuit board with patterns was stuck onto a copper sheet. The hollowed zone was filled with solid paraffin and then covered with a piece of filter paper. The paraffin wax was melted by heating the copper sheet and permeated into the filter paper. Solution to be detected was pipetted on the paper-based device with detective zones. The images of the detective zones were obtained using a microscope with charge-coupled device, which is used for calculating RGBI model to further obtain the solution concentration. Litmus was demonstrated in sample experiments. Results showed that maximum error of measured value of solution concentration to its true value is less than 2% using the presented method.

As it is difficult for traditional control method to realize rotor resuspension after short failure of Active Magnetic Bearing (AMB) system, a new control method containing rotor collision force evaluation algorithm module, rotor position control algorithm module and rotor vibration speed control algorithm module was proposed. Through the evaluation of rotor collision force, the controller automatically selects rotor positon control mode or rotor speed control mode. Based on rigid rotor theory, rotor dynamic model was established to analyze the dynamic responses after short failure of AMB system utilizing different control method. The results proved that the proposed control method can successfully realize rotor re-suspension.

Large transient surges called commutating over-voltage may be generated during the reverse recovery process of diode, which poses a severe threat to the system reliability. However, the commutating over-voltage theoretical calculations are so complicated that it is inconvenient to apply to engineering practice. The characteristics of diode commutating over-voltage are studied by using transient model simulation method. The diode simulation model including reverse recovery characteristics is created by adding a resistance, an inductance and a controlled current source to a common diode simulation model. By taking 1 kV/120 kA DC test system as the research object, the simulation model is created and a commutating over-voltage experiment has been carried out, the relative error between simulation value and measured value is within 8%. It turns out that the transient simulation model not only meets engineering accuracy requirement but also avoids complicated calculations.

The application of machine vision to industrial robots is a hot topic in robot research nowadays. A welding robot with machine vision had been developed, which is convenient and flexible to reach the welding point with six degrees-of-freedom (DOF) manipulator, while the singularity of its movement trail is prevented, and the stability of the mechanism had been fully guaranteed. As the precise industry camera can capture the optical feature of the workpiece to reflect in the camera’s CCD lens, the workpiece is identified and located through a visual pattern recognition algorithm based on gray scale processing, on the gradient direction of edge pixel or on geometric element so that high-speed visual acquisition, image preprocessing, feature extraction and recognition, target location are integrated and hardware processing power is improved. Another task is to plan control strategy of control system, and the upper computer software is programmed in order that multi-axis motion trajectory is optimized and servo control is accomplished. Finally, prototype was developed and validation experiments show that the welding robot has high stability, high efficiency, high precision, even if welding joints are random and workpiece contour is irregular.

Silicon carbide (SiC) metal-oxide-semiconductor field-effect transistor (MOSFET) can work at high switching frequency with low switching loss compared with Si insulated gate bipolar transistor (IGBT). Although Si IGBT and SiC MOSFET have the same MOS-gate structure, the transient characteristics and the gate driver requirements for Si IGBT and SiC MOSFET are different. In order to fully utilize the advantages of SiC MOSFET, the gate driver of SiC MOSFET needs to be optimized to meet some special driving requirements. The paper aims to analyze the characteristics for the new generation of wide band gap semiconductor device SiC MOSFET and proposes a novel gate driver for SiC MOSFET. Meanwhile, the driving protection circuit of SiC MOSFET is also investigated. The performances of the proposed gate driver have been experimentally evaluated by double pulse test (DPT). In addition, the effect of different external capacitors $C_{\mathrm{\text{GS}}}$ and external driving resistances $R_G$ on the switch characteristics of SiC MOSFET is analyzed in detail.

Photometric detection plays a significant role in microfluidics technology. However, the mismatch between the solution concentration and the optical path length will increase detection error. In this study, we proposed a round microfluidic chip for concentration detection to obtain the continuous gradient distribution of concentration. The optimum absorbance can be found by dynamic accurately searching. The solution concentration will be accurately calculated finally according to the relationship between arc length and solution concentration. The overall detection process runs automatically. Under the optimization of injection velocity and concentration, the experimental result shows that the compensation ratio increases as the solution concentration increases. The compensation ratio in the detection of pesticide residue has already reached 14.22% and the reproducibility is acceptable. Therefore, this novel method lays the theoretical foundation for the research of high precision microfluidic photometric detection equipment.

A new self-sensing bearing based on magneto-rheological elastomers (MREs) was proposed in this paper. For the application in vibration isolation system, the effects of force input and semi-active controlled electric current (force and magnetic fields) on the self-sensing characteristics of bearing were simultaneously investigated. The modified anisotropic MRE with multi-walled carbon nanotubes (MWNTs) was studied to improve its electrical properties, and the MWNTs can reduce the initial resistivity from 325.06 to $1.73\mathrm{\text{k}}\mathrm{\Omega }\cdot \mathrm{\text{m}}$. To understand the sensing behaviors of bearing, the experimental system was built. The results confirmed that the self-sensing characteristic presents an obvious monotonous declining trend with the vibration force increases, and the mechanical properties of MRB can be regulated in real-time when they are subjected to varying currents (0–1 A). This finding is significant because it suggests that the bearings based on the modified MREs are potentially capable of being used for vibration absorbers and sensors simultaneously. Based on these results, it is expected to simplify the system structure and improve the response performance of vibration isolation.

The dynamic response of bolted joints subjected to torsional excitation is investigated experimentally and numerically. First, the effects of the initial preload and the angular amplitude on axial force loss of the bolt were studied. Second, the change of hysteresis loops with the increasing number of loading cycles was found under a larger torsional angle. At last, a fine-meshed three-dimensional finite element model was built to simulate the bolted joint under torsional excitation, from which the hysteresis loops were obtained under varying angular amplitudes. The results of numerical analysis are in good agreement with those of experiments.

An electromagnetic generator with magnetic spring and ferrofluid is proposed and designed to harvest low-frequency vibration energy from human body motion. The magnetic spring is formed by gravity and magnetic repulsive force between the fixed and the moving permanent magnets. The ferrofluid is aggregated at both ends of the moving permanent magnet, and the ferrofluid layer between the plastic tube wall and permanent magnet can remove the solid-friction as a fluid lubricant. The electromagnetic generator is used to harvest human motion energy. The measured average load powers of electromagnetic generator with ferrofluid 1.5 g from human body motion are 1.3 mW and 7.5 mW during walking and low running, respectively, which is 30 times more than the measured average power of generator without ferrofluid.

In this paper, we present an effective quality assessment method based on the relation intensity ratio and detail similarity for image quality assessment (IQA) with the full reference image, which first allows us to compute the nonlinear gradient magnitude with Gaussian smoothing of the reference and distorted images and construct the relation intensity ratio and detail similarity between them. Next, the final IQA map is formed by linearly combining the relation intensity ratio with the detail similarity. Finally, we adopt a new pooling strategy which effectively integrates the mean and standard deviation of the final IQA map to accurately predict image quality. Experiments based on two publicly available databases show that the proposed method can provide accurate predictions compared with most state-of-the-art IQA methods.

In this paper, we propose a novel and effective method for removing haze based on a single image, which firstly computes the dark channel of the estimated radiance image by decomposing the dark channel of the haze input image, and the method then estimates the transmission map of the input image. Finally, the scene radiance image is restored by the classical atmospheric scattering model. Experimental results show that the proposed method outperforms He et al.’s method in terms of haze removal.

A blind image deblurring algorithm based on relative gradient and sparse representation is proposed in this paper. The layered method restores the image by three steps: edge extraction, blur kernel estimation and image reconstruction. The positive and negative gradients in texture part have reversal changes, and the edge part that reflects the image structure has only one gradient change. According to the characteristic, the edge of the image is extracted by using the relative gradient of image, so as to estimate the blur kernel of the image. In the stage of image reconstruction, in order to overcome the problem of oversize of the image and the overcomplete dictionary matrix, the image is divided into small blocks. An overcomplete dictionary is used for sparse representation, and the image is reconstructed by the iterative threshold shrinkage method to improve the quality of image restoration. Experimental results show that the proposed method can effectively improve the quality of image restoration.

In order to solve the problems of poor adaptability when setting threshold and the high probability of detecting pseudo-edges in the existing methods of edge detection, the paper proposes an adaptive edge-detection method based on histogram. Multi-scale wavelet transform is used to preprocess the image, the image details are highlighted obviously, and it also can avoid the effect of manual setting filter coefficients. Difference of gray values between the pixels of local area are used to calculate the gradients comprehensively, it extends the gradient direction to four directions. When calculating the gradient of edge pixel, the four directions make the expression of the gradients of edge points more perfect and avoid the edge points missing. The adaptive method is used to compute the threshold of edge-detection, the image is represented by histogram. Then use the ratio of the number of pixels in the bar and the total numbers of pixels to set the initial threshold. The regions on both sides of the initial threshold are used to calculate the high threshold and low threshold until the reasonable error between the current threshold and the previous threshold is very small iteratively. The acquired threshold makes the self-adaptability more reasonable and stronger, it also avoids the detection errors, the connection errors and the pseudo-edges which are caused by setting threshold artificially. The experimental results show that the proposed algorithm of edge detection has a good effect of preserving edge detail and filtering noise of image.

Modern space vehicles face the challenges to obtain more and more accurate attitudes in order to complete the demanding tasks. Onboard star sensors which identify the observed stars in the field of view according to the loaded guide star catalog and output accurate attitude have attracted most interests. Guide stars are usually required to distribute uniformly on the celestial sphere to improve the performance of the star pattern identification. An optimal selection algorithm is proposed to achieve an even distribution of guide stars in this paper. Constellation features are discussed. The mean shift algorithm is analyzed. The idea that distributes stars in the local field of view to constellations is proposed by using the star pair angular separations according to the star positions in the inertial coordinate system. The optimal selection algorithm of guide stars based on star clustering is developed. Its detailed implement procedures are introduced completely. The guide star optimal selection experiment in visible band by using SAO star catalog as the original star data is implemented. It proves that the proposed algorithm has the virtue of simple calculation and easy realization. The obtained guide star distribution is superior to the regression selection algorithm and the magnitude weighted method.

Dissolved oxygen (DO) plays an important role in industrialized freshwater aquaculture. Such deficiencies such as the high cost of water-quality monitoring system and the failure to accurately monitor or describe aquaculture water-quality existed in freshwater aquaculture water-quality monitoring system. Here, a kind of representation method applied to characterize industrialized aquaculture fish behavior in different degrees of DO deficiency is based on three-dimensional (3D) Computer Vision. 3D coordinate values of aquaculture fishes in water acquired from 3D Computer Vision Device by processing aquaculture fish image are applied to represent such parameters as the average activity and height of aquaculture fish in water. This method for representing different behaviors of industrialized freshwater aquaculture fish under the condition of anoxia is realized by using these parameters and combing with the experience of aquaculture. The results show that the representation of industrialized freshwater aquaculture fish based on 3D Computer Vision System can be applied to describe industrialized aquaculture fish behavior and effectively compensate for the shortfall spatial location of aquaculture fish unable to acquire from 2D monitoring system, which is helpful for the accurate and reasonable control of DO in aquaculture.

Clouds produce obstacles which hinder satellites from collecting clear ground information. Removing clouds from satellite images becomes a promising way to recover valuable information efficiently. It reduces the demands for weather and improves the image collecting flexibility. In this paper, a cloud removal algorithm from single images is studied. Firstly, the fundamental principle of the dual tree complex wavelet transformation (DTCWT) is introduced briefly. The scheme of dividing the clouds from the scenery and the background roughly according to their frequencies after the image is decomposed by DTCWT is discussed. Secondly, a new method to estimate the atmosphere transmission coefficient is designed after analyzing the atmosphere radiation model and the dark channel priori theory. Then, a cloud removal algorithm from single images is proposed by combining DTCWT and the improved atmosphere transmission. Its implement procedures are described completed. Image processing experiments are carried out and evaluated. The results prove the proposed algorithm is satisfactory and superior to algorithms based on the weighted wavelet coefficients and the dark channel prior.

For solving the advanced manufacturing fault diagnosis issue, a novel zonotopes estimation-based fault diagnosis algorithm is proposed in this paper. By using the intersection of the convex polytope and the tight strip, the fault diagnosis problem is changed into the analysis of the set membership outer bound computation. If the feasible set is detected empty, the set membership filters are designed. The minimal volume is also calculated and the selected zonotopes can be viewed as the approximate boundary. The simulation results show the effectiveness and practicability of the presented fault diagnosis algorithm.

To realize sensor network-based positioning, the use of conventional techniques based on fingerprints has considerable cost due to the need of establishing in an offline manner a fingerprint database. Besides, it is also time-consuming when searching the database for calculating the localization solution. To address these drawbacks, we first propose to utilize the inverse distance weighted (IDW) interpolation method to improve the spatial resolution of the fingerprint database. The genetic algorithm learning machine (GAELM) is introduced to speed up the database lookup while enhancing the positioning accuracy of the fingerprint-based localization. Experiments show that the proposed Extreme Learning Location algorithm based on Insufficient Fingerprint information (ELL-IF) offers improved positioning performance over the BP-neural network (BP-NN)-based and extreme learning machine (ELM)-based methods.

Fine resolution frequency estimation of a single-tone complex sinusoidal signal in the additive white Gaussian noise is of importance in many fields. In this paper, a generic analytical expression is proposed to refine the residual of a dichotomous search, leading to an estimator with much less iterations than the conventional dichotomous search estimator. Compared with other existing estimators, the proposed estimator has a better trade-off between performance and computational complexity. Simulation results demonstrate that the root-mean-square error (RMSE) of the proposed estimator is closer to the Cramer–Rao lower bound (CRLB) than other estimators over the whole frequency interval when the signal-to-noise ratio (SNR) is above a threshold.

Voice conversion (VC) is a technique that aims to transform the individuality of a source speech so as to mimic that of a target speech while keeping the message unaltered. In our previous work, Gaussian process (GP) was introduced into the literature of VC for the first time, for the sake of overcoming the “over-fitting” problem inherent in the state-of-the-art VC methods, which gives very promising results. However, standard GP usually acts as somewhat a smoothing device more than a universal approximator. In this paper, we further attempt to improve the flexibility of GP-based VC by resorting to the expressive kernels that are derived to model the spectral density with Gaussian mixture model (GMM). Our new method benefits from the expressiveness of the new kernel while the inference of GP remains simple and analytic as usual. Experiments demonstrate both objectively and subjectively that the individualities of the converted speech are much more closer to those of the target while speech quality obtained is comparable to the standard GP-based method.

This study investigated whether the effect of circadian effect can be characterized by midnight pupil size. A phosphor-converted LED and an RGB LED with the same photopic luminance and color temperatures but different circadian luminance were selected as the experimental light sources. The steady-state pupil diameters stimulated by these two LEDs’ lighting were obtained by the infrared video, which were affected by photopic and circadian effects together. Twenty healthy volunteers were tested at different times of a day for five working days. It was found that the steady-state pupil size stimulated by these two LEDs were significantly different at midnight, but showed no significant difference in daytime, which indicated the circadian effect can be characterized by midnight pupil size. This study was very important for the design and evaluation of healthy lighting.

In order to eliminate the effect on the grid current caused by the background harmonic voltage and the reference signal on the grid connected multi-inverter, this paper adopts the double closed-loop feed-forward control strategy. This strategy is based on the inductor voltage and the grid-connected current, and the integrated control strategy of quasi-proportional resonance loop parallel to a specific harmonic compensation loop. Based on the closed-loop model of multiple inverters, the change curves of the transfer function of the two control strategies are compared with the feed-forward control and the composite proportional resonance. The two corresponding control methods are used to analyze the current quality of the multi-inverter impact. Finally, the MATLAB/Simulink simulation model is set up to verify the proposed control strategies. The simulation results show that the proposed method can achieve better tracking of the sinusoidal command signal at the fundamental frequency, and enhance the anti-interference ability of the system at the 3rd, 5th, and 7th harmonic frequency.

In order to improve the fault diagnosis rate and efficiency of diesel engine, the PCA-RBF neural network as a new algorithm was constructed by combing the character extraction ability of PCA with the nonlinear approximation ability of RBF neural network. Firstly, eight factors which affected the fault types of diesel engine were analyzed and three principal components were extracted by PCA. Secondly, the data obtained from the three principal components were taken as the input of RBF neural network which was trained and tested. Finally, the PCA-RBF neural network was verified through simulation. The simulation results show that the network has fewer training steps, less training and higher training accuracy.

The simulation of Chinese pulse diagnosis via acquisition and qualitative description of temporal and spatial pulse signals is important for objective pulse diagnosis in traditional Chinese medicine. In order to simulate pulse diagnosis by fingers, we developed a novel pulse signal detection device based on the airbag-type flexible probe, in which the pulse-taking pressure was adjusted using lever principle. A contact film was fixed at the tip of the probe, and two cameras were used to synchronously taking the deformation image of the contact film. Grid-pattern lines were printed on the contact film. Image coordinates of the intersection points were detected using ridge line fitting method. Then, the 3D coordinates of the intersection points were calculated based on binocular vision measurement principle and the deformations of the contact film was reconstructed through surface fitting. Finally, the 3D pulse image series containing spatial and temporal information were acquired. Using the acquired 3D pulse images, the pulse conditions can be quantitatively described, indicating the 3D pulse images are an effective way of quantitatively describing pulse conditions.

The shear waves have been taken into consideration to deduce the theoretical model of the scattering of isotropic circular solid cylinders in a fluid medium. The theoretical analysis has been done based on the wave equation and the three classical boundary conditions. The analytical solution of the far-field pattern for this scattering issue has been derived and the pressure in the scattered wave can be given. And the finite element method (FEM) has been used to solve the underwater acoustic scattering problems. Numerical solution of the resulting was discussed in three directions. The following work is to study the scattering problems of collection of cylinders with arbitrary cross-sections and any numbers. The conclusions drawn in this paper could contribute to explore the acoustic propagation characteristics of the two-phase media.

This paper designs a smart antenna system for the connective vehicle infotainment. It provides a whole software solution for the Vehicle Head Unit independent with OS system. It includes worldwide analog and digital radio and TV, full radio stacks, FM to DAB on web radio seamlessly, vehicle networking management. This paper is to define the interface between the smart antenna module and the Head Unit. The physical link between the smart antenna and the Head Unit is Ethernet, the interface is based on several protocols based on IP protocol such as UPnP, HTTP to receive a radio signal and use it. Thanks to the open source structures and integrating all the radio stacks, this system is much easier for OEM vehicle companies and Tier one suppliers to adopt the smart antenna into the infotainment Head Unit. It saves them a lot of developing time and cost.

When multiple inverters are connected to the grid, due to the uncertainty of the internal model and the presence of external disturbances, the grid-connected current waveform will deviate from the expectation. In this paper, Active Disturbance Rejection Control (ADRC) strategy is presented to suppress the internal and external perturbations, moreover, the first-order ADRC parameters are designed. Finally, MATLAB/Simulink is utilized to verify the control strategy of ADRC. The simulation results demonstrate that the ADRC method can suppress the disturbance effectively.

Performance improvement for optical generation of octupling millimeter wave signal based on dual-parallel Mach–Zehnder modulator (MZM) without optical filtering through optimization of modulation index of MZM is proposed. Theoretical derivation and simulation verification are provided for the octupling millimeter wave generation scheme, extinction ratio (EXT) of practical MZM is considered. The effect of imperfect EXT of commercially available MZM on optical sideband suppression ratio (OSSR) and RF spurious suppression ratio (RSSR) of the generated octupling optical millimeter wave is investigated. The results show that the performance of generated octupling millimeter wave with modulation index optimization is stable and insensitive to the EXT of MZM. While for the approach without modulation index enhancement, an additional filter is required to generate high quality optical millimeter wave.

Three-dimensional (3D) fluorescence spectra and synchronous 3D fluorescence spectra are used to analyze the concentration of Amaranth in commercial cocktails by Parallel Factor Correction Algorithm. The former average relative error of the Amaranth in the commercial cocktail is 15%, and the latter average relative error is less than 4.0%. The results show that the synchronous fluorescence spectrum can improve the sensitivity and selectivity of the fluorescence spectroscopy technique greatly, and it has obvious advantages on analysis of the complex multi-components system. Finally, the content of Amaranth in the cocktail (about $(3.5\pm 0.2)\mathrm{\text{mg}}\cdot {\mathrm{\text{kg}}}^{-1}$) is much lower than that of the national standard by using synchronous data combined with parallel factor analysis of the content of Amaranth.

The modern telecommunication has come into the Long-Term Evolution (LTE) era. The whole transmission network gradually has evolved to the IP network. The overall network organization is likewise becoming more and more similar to the metropolitan data network. A mobile Packet Transport Network (PTN) transmission network construction is investigated. The specific bearing requirements of time division long-term evolution (TD-LTE) are analyzed. The topology structures of L3 and L2/L3 and the equipment applications in different network levels of the PTN network are discussed. The existing problems concerning the circuit scheduling, disaster tolerance requirement, security connection and business pressure are analyzed in detail. Their corresponding optimization strategies are presented. Finally, the network level having a clear, robust and complete coverage is reached. It proves that the optimized network can meet the third level of the disaster tolerance international standard SHARE 78.

In industrial manufacturing system, there exists an urgent demand for dual-motor drive system running at the same speed. This paper adopts a parallel structure to design dual-permanent magnet synchronous motor (PMSM) system with a global fast terminal sliding mode control (GFTSMC) technique optimizing speed tracking performance of dual-PMSM servo system. For each PMSM system, a GFTSMC strategy is introduced. Compared with common sliding mode control strategy, this strategy avoids chattering phenomenon in that it does not contain switching terms. Also, it can make the output speed reach the reference value in finite time, along with a shorter convergence time and a better tracking precision. Since external disturbances may cause the speed difference between two motors, a speed synchronization controller is designed to enhance their synchronization by compensating error between speeds of two motors. Considering the complexity of control laws, S-function in MATLAB is adopted to design the GFTSMC. The validity and robustness of the hybrid scheme are verified by MATLAB simulations.

Denoising autoencoder is a data compression algorithm in deep learning, and it can successfully extract features which contain main information of the input data. However, this method is trained in an unsupervised manner and cannot aim at extracting the specified information. Therefore, the input data will be classified into a class label by main but not necessary information using traditional denoising autoencoder, and redundant information reduces the classification accuracy. To solve this problem, denoising class autoencoder presented in this paper is adopted to train a classification function and extract class features. Both the proposed approach and traditional methods are trained on MNIST handwritten digit database with mini-batch gradient descent, moreover the recognition result of the proposed approach demonstrates the effectiveness in comparison with conventional methods.

The energy consumption of numerical control (NC) machine tools is mainly from the cutting process. Since the cutting process has multiple characteristics, such as nonlinear, multi-variable and time-varying characteristics, it is difficult to establish an accurate model of the cutting energy consumption by both the static modeling and the traditional mechanism modeling. In this paper, the unscented Kalman filter neural network (UKFNN) is proposed to model the cutting energy consumption of NC machine tools. In the resulting model, the unscented Kalman filter (UKF) is adopted to update weights and thresholds of the neural network in real time, improving the generalization ability and the accuracy of the model. Experimental results show that the proposed method is effective and reliable to model the cutting energy consumption of NC machine tools.

This paper considers many-objective flexible job shop scheduling problem (MaOFJSP) in which the number of optimization problems is larger than three. An integrated multi-objective optimization method is proposed which contains both optimization and decision making. The non-dominated sorting genetic algorithm III (NSGA-III) is utilized to find a trade-off solution set by simultaneously optimizing six objectives including makespan, workload balance, mean of earliness and tardiness, cost, quality, and energy consumption. Then, an integrated multi-attribute decision-making method is introduced to select one solution that fits into the decision maker’s preference. NSGA-III is compared with three multi-objective evolutionary algorithms (MOEAs)-based scheduling methods, and the simulation results show that NSGA-III performs better in generating the Pareto solutions. In addition, the impacts of using different reference points and decoding methods are investigated.

This paper focuses on multi-objective dynamic flexible job shop scheduling problem (MODFJSP) with machine breakdown. First, a multi-objective dynamic scheduling model is established, with objectives to minimize makespan and total machine workload. Second, according to the processing status of faulty machine, a hybrid rescheduling strategy including transfer rescheduling strategy and complete rescheduling strategy is proposed to react to stochastic machine breakdown. The performance of two rescheduling strategies is analyzed in terms of the scheduling efficiency and its stability, from the delay extent and initial scheduling deviation, respectively. Besides, the optimal adaptation conditions of both scheduling strategies are obtained. Furthermore, the non-dominated sorting genetic algorithm (NSGA-II) is employed to solve the constructed model. Experimental results demonstrate the effectiveness of the proposed strategies on reducing the impact of machine breakdown in real scheduling.

In this paper, a multi-objective flexible job shop scheduling problem (MOFJSP) was studied systematically. A novel energy-saving scheduling model was established based on considering makespan and total energy consumption simultaneously. Different from previous studies, four types of energy consumption were considered in this model, including processing energy, idle energy, transport energy, and turn-on/off energy. In addition, a turn-off strategy is adopted for energy-saving. A modified shuffled frog-leaping algorithm (SFLA) was applied to solve this model. Moreover, operators of multi-point crossover and neighborhood search were both employed to obtain optimal solutions. Experiments were conducted to verify the performance of the SFLA compared with a non-dominated sorting genetic algorithm with blood variation (BVNSGA-II). The results show that this algorithm and strategy are very effective.

Considering the uncertainty in real manufacturing workshops, the fuzzy flexible job shop scheduling problem (fFJSP) is addressed, in which the triangular fuzzy number is used to represent the processing time. A discrete flower pollination algorithm (DFPA) is proposed in this paper to minimize the maximum fuzzy completion time. Flower pollination algorithm (FPA) is inspired by the pollination process of flowering plants, which realizes global search and local search by means of cross-pollination and self-pollination of flowers in nature. DFPA extends to FPA by introducing discrete operator during iterations. Simulation results on instances validate the effectiveness and feasibility of this algorithm compared with particle swarm optimization.

This paper concentrates on energy conservation in flexible manufacturing system. In addition to the energy saving of single machine tool, it is significant to reduce energy consumption in the two sub-systems of process planning and shop scheduling. Compared to traditional methods that consider the two sub-systems separately, integrated optimization of these sub-systems further improves the energy efficiency of the job shop. Furthermore, the transportation of jobs and semi-manufactured jobs in the process have been ignored in previous research, which has a great influence on the process routes selecting, machine dispatching and energy consumption. Therefore, this paper proposes an energy-aware multi-objective integrated optimization model of process planning and shop scheduling considering transportation. Parameters are optimized simultaneously including work piece machining feature selecting, process method selecting, processing sequence and machine dispatching of each job. The non-dominated sorting genetic algorithm is adopted to minimize the total energy consumption and makespan. Finally, a case study using the proposed model is employed to verify that energy consumption of transportation has authentically influence on total energy consumption and scheduling scheme.

With the development of manufacturing technology, traditional empirical formulas are hard to deal with the relationships between complex manufacturing techniques and surface roughness. Current data fitting-based modeling methods often do not consider the influence of various factors on surface roughness and lack data cleaning capability. This paper proposes a method to reduce the dimension of data features, which includes the Lasso model to determine the correlation degree of processing parameters and roughness, and solves the possible sparse coefficient relationship between processing parameters and roughness. The ridge regression is also introduced to predict the workpiece surface roughness. The results show that compared with the existing prediction model, this prediction method has high accuracy when given a small amount of training data.

In this paper, the degradation of mechanical properties for eccentrically compressed members was investigated by testing the bearing capacities under the combined effect of load, water permeation and salt environment of chloride and sulfate. The results showed that: (1) the bearing capacity of eccentric compression members under “Seepage stress field” increased with the increase of time; (2) when suffering the combined action of chloride and sulfate, the bearing capacity of eccentric compression members under “Seepage stress field” increased in the early stage of corrosion. However, the bearing capacity decreased afterwards. The higher the ions concentration, the greater the descent; (3) the greater the eccentricity, the greater the vertical displacement of eccentric compression members and the smaller the ultimate bearing capacity.

This paper aims to extract the exact defect characteristics of the thin film surface of the lithium battery by sparse decomposition algorithm. An appropriate atomic function was selected and the sparse decomposition iteration was conducted on the defect images in the overcomplete dictionary. This value from observation method was taken as the empirical value and applied as the iteration termination condition of the sparse decomposition. Then, the denoised defect images were obtained. The results reveal that the sparse decomposition has a far superior denoising performance to that of the median filtering technique, and can better restore the thin film defects of the lithium battery.

The main purpose of this paper is to present a method for dynamic modeling and simulation of the translational joint with friction and clearance. The sizes of the clearances and the impacts between the slider and the guide in the translational joint can be neglected when the clearance sizes are very small. The geometric constraints of the translational joint are treated as bilateral constraints. The contact forces acting on the slider are reduced to the forces on the slider corners. The LuGre friction model is used to describe friction between slider and guide, because it can capture the variation of the friction force with slip velocity and the slider motion with stick–slip phenomenon. The problem of computing the normal forces on the slider is formulated and solved as a horizontal linear complementarity problem (HLCP), which is embedded in the event-driven method. Finally, a numerical example is considered and numerical results are presented to show the feasibility and the effectiveness of the method.