Abstract:Aiming atthe high production cost and the large occupied area of throughsiliconvias(TSVs)in threedimensional networkonchip,the test scheduling of 3D NoC is researched.To reduce the hardware overhead and improve the utilization rate in a limited number of TSVs, a new methodusing improved quantuminspired evolutionaryalgorithmis proposed, which is to configure TSVs in each layer according to the demandand allocate TSVs to each core reasonably and effectively. Moreover,the impact of TSVs’ allocation on testtime under the constraint of the shortest test time is discussed.To improve the ability of searching and converge speed, the strategy of dynamic adjustment of rotating angle of quantum rotating gate and quantum mutation are designed in the algorithm, which can prevent the algorithm from running into the local optimization solution effectively.Taking ITC’02 standard circuit as the test object, experiment is conducted,and the experiment results demonstrate that the proposed method can converge to the optimal solution quickly to reduce the total test time, and the number of TSVs can be optimized to improve the TSVs’ utilization.

Abstract:To improve the testing efficiency of 3D NoC, a variable weight time Petri net model was established in combination with the characteristics of 3D NoC test. The weight of the algorithm was related to the transition delay. Meanwhile, the Petri net given the dynamic transition. On this basis, we planned the scheme by taking transition firing sequences as parallel test tasks of IP cores, and used the improved bat algorithm based on the simplified bat location update equation to solve this model. The rules of bat location update were integrated into the Petri net evolution rules to simplify the reasoning process, avoid the algorithm premature and improve the convergence rate. The ITC’02 test benchmarks were used as the experimental objects. The simulation results show that the 3D NoC test planning problem can be effectively described in this proposed model, and 13.9% of the test time optimal rate and improved test efficiency can be obtained.

Abstract:How to optimize the test time under multiple constraints is an urgent problem to be solved in the networkonchip (NoC) testing. An optimization method of NoC test scheduling based on sine cosine algorithm (SCA) is proposed. A parallel test method using dedicated test access mechanism (TAM) is adopted, and a test scheduling model for NoC is built to satisfy the power consumption and pin constraints. To achieve test time minimization, the population fluctuation with the sine and the cosine function around the optimal solution, and a group of random operators and adaptive variables are adopted. Comparing experiments on the ITC’02 test benchmarks test show that the proposed algorithm can achieve shorter test time than that of the particle swarm optimization (PSO) algorithm.

Abstract:As digital microfluidic biochip is widely applied in biochemical detection fields, it is required to test the biochips completely and efficiently to guarantee the reliability of biochip. With the expansion of the size of biochip, the fault testing problem of digital microfluidic biochip is getting more and more complex. Aiming at the catastrophic faults of biochip, a test path scheduling based on combined genetic and colony algorithm is proposed to improve time efficiency of testing method. Firstly, the scheduling optimizes the conversion process of fault testing model. Then, some global excellent test paths are generated by using the global property of genetic algorithm, and the initial pheromone distribution of ant colony algorithm is formed according to these excellent test paths. Finally, the optimal test paths are searched by using ant colony algorithm. This scheduling is suitable for offline and online testing, and it can also be used for rectangle and nonrectangular biochip. The experiment results show that this scheduling can improve the efficiency of the conversion process of fault testing model. At the same time, this scheduling can improve the astringency and the time efficiency of testing algorithm in the case of getting optimized testing paths.

Abstract:The stability and safety of digital microfluidic biochip have high requirements in biochemical experiments. To ensure the accuracy of the experimental results, the chip needs to be tested.In this paper, proposed an algorithm for optimize online test route, based on particle swam optimization and the scheme established constraints path which used in the experimental process, application of exchange mechanism in test route sequence in order to satisfy the constraints in the biochemical process, in order to inproving the algorithm’s convergence speed and balance the gobal search ablity and local search ablity, this paper adjusting the inertia weight of the algorithm and set the convergence rate factor and degree of aggreation of the algorithm.Simulation experiments were carried out to select 15×15 array model, 7×11 array model, 7×7 array model for simulation experiments, the experimental results show that the method can be used for chip online testing, effectively shortening the test route, and improve the test efficiency.

Abstract:Photonic networkonchip (PNoC) has been a new trend and example for next generation multiprocessor system. Microring resonator（MRR）is the key component in PNoC. However, MRRs are sensitive to environmental temperature and prone to be faulty. Therefore, how to detect a MRR fault is a key problem. An approach based on fault check graph is proposed. An Nport photonic router is modeled as a complete weighted directed graph called preFault Check Graph, and MRR model is created. By the complete weighted directed graph and fault simulation, the proposed method is established with fault check graph and MRR model. The experimental results prove that the proposed approach is effective with the single fault simulation and double fault simulation.

Abstract:In order to improve the correct classification rate of analog circuit fault diagnosis and recognition, a simulation circuit fault diagnosis method based on lifting wavelet transform and chaotic firefly algorithm (CFA) is proposed to optimize LSSVM parameters. Firstly, the wavelet transform is applied to the output voltage signal of the measured circuit. Then, the transformed data is analyzed by factor analysis method, and the optimized data is taken as the fault feature set of different modes. Finally, the obtained fault feature set as sample is imported into the CFALSSVM model for troubleshooting. The experimental results show that the fault diagnosis accuracy of this method is more than 98%, which improves the diagnostic performance and can be applied to the fault diagnosis of analog circuits.

Abstract:Ptype metallic electrode in IIIV microcavity lasers traditionally adopt the TiPtAu alloys mode, while the Ti layer has a strong optical absorption for the laser at the communication wavelength 1 550 nm, which is not suitable for the application in photonic integration. In order to replace the traditional alloy method, this paper calculates the optical model quality factor in the microcavity under the constraints of single metal Al and Ag metal using analytical method, and found that these two new electrodes have smaller optical losses. In addition, experiments were carried out on the microcavity lasers with electrodes of metal Al and Ag, respectively, and the results were well consistent with the theoretical predictions. Moreover, the two electrodes are cheap, simple and compatible with CMOS process, so they have high application prospects in photonic integration.

Abstract:In order to solve the contradiction between the features of the large scale, wide distribution and limited bandwidth for wireless sensor networks (WSNs) and upper bound of the reliable and realtime data communication, a routing communication model based on minimum traffic is proposed in this paper. Using the traffic balance between the received data and the transmitted data in WSN node as an entry point and considering the constraint condition for WSN communication and the realtime requirement of data transmission, the routing communication model based on the minimum traffic is established for WSN communication applied to smart distribution grid. Hamilton function of WSNs communication model applied in smart distribution grid is constructed by using the method of Pontryagin’s extreme values. Utilizing uniqueness of extreme existence and combining with necessity, the proposed method can judge whether the wireless sensor nodes in the optimal path. The process of solving the optimal control model is also given. The communication performance including delay time, network energy consumption, transmission capacity and error rate of transmission data is tested for the proposed routing method. The results show that the proposed model can fully meet the requirement of smart distribution grid communication.

Abstract:In order to research the location identification technology of smart skin under static load response, a static load identification verification system is presented and designed. An optical fiber Bragg grating (FBG) orthogonal symmetrical sensing network is constructed, static load position identification of composite plate is carried out, where carbon fiber reinforced plastics (CFRP) is as a research object. The loading position is identified by using data correlation weighting coefficient theory and algorithm, and the test shows that the positioning accuracy is less than 2 cm. The design of static load identification verification system provides new idea and method for CFRP skin structure health monitoring.

Abstract:This paper explores the effects of magnetic induction on motion rehabilitation in stroke patients. The extreme low frequency (16 Hz) and low intensity (20 mT) pulsed magnetic field was applied on the affected brain side of 8 stoke patients. The magnetic fieldinduced electroencephalography (EEG) signals in resting state were collected and analyzed. The JebsenTaylor hand function test (JTT) behavioral parameters combined with the simplified FuglMeyer motion function and the modified Barthel index rating scales were utilized to evaluate the rehabilitation effect. The experimental results show that after magnetic induction, the scores of the rating scales were significantly increased and the power of the high frequency rhythm increased upon the motion cortex. The study shows that the magnetic induction technique has positive effect on the rehabilitation of the stroke patient and could be combined with the traditional rehabilitation therapy to assist the neural rehabilitation.

Abstract:In order to solve the problem that the basic particle swarm (PSO) to optimize the parameter of SVM is easy to fall into local optimum, this paper proposes a modified classifier that uses the improved particle swarm optimization (IPSO) to optimize the parameter of SVM (IPSOSVM) by introducing the new dynamic inertia weight, global neighborhood search, shrinkage factor and mutation operator of genetic algorithm. The classification result is tested by the common datasets named Iris, Wine and seeds from UCI machine learning repository, the result shows that IPSOSVM classifier is better than GSSVM, AFSASVM, GASVM and PSOSVM classifier in terms of classification accuracy and classification time. The better convergence ability and speed of the IPSOSVM classifier are verified by fault diagnosis of SallenKey bandpass filter, fouropamp biquad highpass filter and nonlinear rectifier circuit.

Abstract:The working conditions of mechanic system in real life are generally studied by analysis of signals so that the exact conclusions will be drawn. These signals emanating from mechanic system commonly contain a mixture of different oscillations. For a reliable conclusion, it is necessary to separate a set of physically meaningful modes from the mixture and background noise. Based on that, a new method for bearing fault extraction is proposed in this paper. At first, a novel decomposition algorithm named empirical wavelet transform (EWT) is employed to decompose the fault signal into a set of AMFM components that have a compact support Fourier spectrum. And then, KL divergence method is used to select the sensitive component. Finally, the fault characteristic frequency is extracted by a new demodulation method called energy operator of symmetrical differencing (DEO3S) that can restrain the end effect, and the instantaneous frequency is obtained at the same time. The results of the simulation and bearing fault diagnosis experiments indicate that the method can effectively extract fault characteristic frequency, certifying its feasibility and superiority in comparison with the previous methods.

Abstract:In order to improve the reliability of the coal mine power supply system, the simulation experiment in different voltage, current, power factor and environmental relative humidity conditions is carried out. The influence of different experimental parameters on the arc fault is analyzed, eigenvector constituted by the number of passing zero, normalized variance and covariance of current signals of adjacent five cycles on series arc fault are extracted, and a diagnosis model of series arc fault is established based on random forest classification algorithm. The training samples and test samples constituted by eigenvector of the normal operation and fault arc current signals are served as the input of random forest model, which are sorted to further diagnose whether the series arc fault is occurred. The results show that the method can effectively realize the diagnosis of series arc fault on mining electric connector.

Abstract:MEMS gyroscope has the advantages of small volume, low cost and easy integration, but its low accuracy greatly limits its application in practice. The measurement accuracy of MEMS gyroscope can be improved by using multisensor fusion technology for error compensation, so people have proposed many kinds of data fusion methods for improving the measurement accuracy of the MEMS gyroscope. In this paper, the multiscale fusion method, the Kalman filter fusion and the wavelet threshold fusion method, are compared and analyzed. Theory analysis and experiments results show that, comparing with the Kalman filter fusion and the wavelet threshold fusion method, the multiscale fusion algorithm has better performance on standard deviation, signal to noise ratio, power spectrum, and the Allan variance and so on, and it has a wider scope of the application.

Abstract:The online measurement of turbine wetness has great theoretical significance and practical value to the safety and economic operation of steam turbine. According to the basic theory of perturbation method, a method is proposed to realize the turbine wetness measurement by inverted microstrip patch resonator, which has a simple construction with high sensitivity. The dielectric constant changes with the steam wetness change. The resonant frequencies of different dielectric constant are simulated based on the principle of microstrip patch resonator with the wet steam as sample substrate of resonator. The effects of sample thickness, substrate thickness and dielectric permittivity on the frequency offset are discussed. The microstrip patch resonator model is designed and simulated on the HFSS and CST. The study and simulation results show that the model is suitable for the online measurement of steam wetness of steam turbine. When the steam wetness changes 1%, the resonator frequency offset is about 18 kHz, which is about 3.6 times of frequency offset of microstrip slot resonator. The model is conducive to the accurate measurement of the steam wetness.

Abstract:The anti collision algorithm determines the operating efficiency of RFID systems. An improved hybrid query tree (IHQT) anticollision algorithm is presented in this paper, which is based on the query tree algorithm. When collision occurs, the collided node will split into some child nodes. IHQT addes a branch prediction stage before the reader query collided tags to avoid the idle slots. The branch prediction method presented in this paper can predict the location of idle slots accurately. When the reader generates new query prefixes, those query prefixes which visit the idle slots will be avoided successfully. The performance analysis of algorithm and simulation results show that under the premise of a small increase even significant decrease in the query times of reader, the number of timeslots and throughput of IHQT algorithm are significantly better than the existing query tree anticollision algorithms.

Abstract:With the development of industrial robots and modern industrial, the more performance requirements for industrial robots are needed. To improve production efficiency and product quality, intelligent, high speed and high precision are essential requirements for industrial robots. In summary of domestic intelligent beer bottle mouth defect detection method based on machine vision, highspeed and highaccuracy is still a problem to be solved. This paper presents the fourcircle positioning method based on circle fitting assessment method, which greatly improves the accuracy of bottle mouth detection area, and the smart bottle mouth defects detection method based on subregion hysteresis thresholding segmentation of projection features. Collected 488 image tests, the detection accuracy is 99.4%, the average speed of detection is 38 ms. The algorithm proposed in this paper has high detection speed and high detection precision, it can be well applied in the modern industrial robot with high speed and high precision.

Abstract:In this paper, a fast and accurate identification of unsound kernels of wheat (black embryo, wormhole and damaged) is introduced via the convolution neural network (CNN) model. The hyperspectral images of 116 bands in the range of 493 to 1 106 nm, which includes normal kernels (484 grains), black embryo kernels (100 grains), wormhole kernels (100 grains) and damaged kernels (100 grains), are collected. We take one sample out of every five bands to construct the training sets of the 24 bands respectively, and use the proposed model to establish the identification model of unsound kernels of wheat. Experimental results indicate that, by using the proposed model, the recognition rate of black embryo, wormhole and damaged grains is maintained at above 94%, 95% and 92% respectively. We further improve the model by modifying the learning rate and the number of iterations, which end up improving the average recognition rate of black embryo, wormhole and damaged grains in each band by 0.624%、0.47% and 0.776%. We combine the hyperspectral imagery of all 24 bands to reconstruct the training set and retrain the CNN model. The total recognition rate of black embryo, wormhole and damaged grains was increased by 0.31%, 0.13% and 0.46%, respectively. For our studies, we find that the accuracy of unsound kernels of wheat grain recognition, can be effectively improved using hyperspectral data and the proposed CNN model.

Abstract:3D neuron terminal points could be very good seed points in neuron tracing algorithms. Previously, a rayshooting model to detect neuron terminal point was proposed by analyzing the intensity distribution characteristics of the neighborhoods around the terminal point candidates. However, the length of the shooting rays and the number of zslices that should be considered in this model are fixed, its accuracy would be seriously affected when handling datasets where the diameter of the neuron varies much. Thus, an adaptive rayshooting model is proposed by changing the length of the shooting rays and the number of adjacent slices according to the local diameter of the neuron obtained by the MSFM (multistencils fast marching) method and Rayburst sampling algorithm. Compared with the previous work, the experimental results show that the proposed method could improve the detection accuracy by about 10%.

Abstract:Definition of object tracking is identifying the moving targets from complex background, and it should track the target accurately and continuously. In occlusion, deformation, complex background conditions robust tracking target is still a challenging problem to be solved. A novel online object tracking algorithm is proposed for the occlusion and deformation with sparse prototypes, which exploits local linear embedding (LLE) algorithm with sparse representation scheme for learning effective appearance model. LLE is a classic manifold learning algorithm. In the algorithm, the neighbor points weight of each point remains unchanged in translation, rotation, scale changes. Thus, it can be used to extract the essential characteristics of target and find the inherent law of data. Firstly, the algorithm uses the local linear embedding algorithm to extract low dimensional characteristic. Then the sparse prototype is composed of the base vector which is extracted from the low dimensional characteristic and trivial templates. It can be used to update templates. This algorithm maintains the advantages of the original sparse tracking method to occlusion, and has a good robust tracking effect of deformation object. The experimental results show that the proposed tracking algorithm is better than the other seven commonly used algorithms in the nine video sequences.

Abstract:In this paper, with the combination of convolutional autoencoders (CAE), an algorithm named stacked denoising autoencoders based on Fisher criterion (FSDAE) is proposed to solve the problem of the difficulty of manual features extraction and the limitation of defect samples on traditional fabric defect detection. Firstly, the sparse autoencoder (SAE) is used to obtain the sparse characteristics of the small patches cut out from the original images. Secondly, the CAE network parameters are initialized by using the sparse characteristics and the lowdimensional features of the original image are extracted. Finally, the features data are sent to the FSDAE network for defect detection and classification. The experimental results show that the algorithm can effectively extract the classification characteristics of the fabric image, and the detection rate of the fabric defect is improved by adding the Fisher criterion.

Abstract:The precision detection and control method of the temperature field in the furnace chamber is the core of precision quantitative analysis. It has uncertainty characteristics of operation mode with high nonlinear, strong coupling, multiple disturbance and so on. Based on the reliability evaluation method of evidence theory, this paper presents a method to evaluate the uncertainty of the temperature field heat flux distribution in the furnace by using the information fusion technique. A method of combining heat conduction mathematical model with actual data measurement is established. Based on the Pignistic index function optimization algorithm, the Pignistic vector of the similarity measure method is established. To solve the system temperature field transient distribution of heat flux and the estimation of the thermal loss parameter, the measurement results were corrected several times by acquiring static correction factor. The experimental verification shows that the method can evaluate the uncertainty and accuracy of the measurement accuracy of the thermal analyzer.

Abstract:It hasattractedattention of the researchers worldwide to establish an equivalent model with high accuracy and simple calculation, which will be used to analysis harmonic of nonlinear load in power system. Norton model is widely used in nonlinear load analysis because it is simple to calculate. However, the interaction between harmonic current and voltage is not considered by the traditional Norton model, which has a large error in the analysis results. Therefore, it is necessary to change its structure in order to improve the accuracy of Norton model.In this paper, the harmonics which has the effects on nonlinear loads are classified, the additional current harmonics are represented by different controlled sources. Then, a modified Norton model is proposed based on the traditional Norton model. Finally, by using the two models under different voltages, the results show that there are 2 orders of magnitude difference in the equivalent impedance value of the traditional Norton model, butthe results of modified Norton model have little change and can be approximately equal. Obviously, the modified Norton model proposed in this paper has higher accuracy and stabilitycompared with the traditional Norton model.

Abstract:When the single soft sensor model is used for the dynamic liquid level prediction, there are many shortcomings such aspoor generalization ability, weak adaptive ability, etc. In order to solve these problems, a soft sensor modeling method based on AdaBoost ensemble learning algorithm is proposed in this paper. The proposed method focuses on effects of the prediction error to the weight of the modeling samples and weak learning machine, therefore which is more suitable for the regression model prediction.In practical production,dynamic and changing working conditions during operations may lead to failure of the soft sensor model. In order to solve this problem,a small amount of patrolmeasuring data of the dynamic liquid levelis used to evaluate the original model, and then the similarity principle is used to add new data on the basis of the original model. And on this basis the weight of the new data is used to update the weak learning machine to become the strong learning machine model to dynamically adapt to the new production conditions.The simulation results using the real operation data of the oil well show that the proposed method has strong adaptive ability for fluctuation in production and can improve the generalization ability and the prediction accuracy of the soft sensor model.