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Counters have more and more applications in industrial production and become a necessities. The tradition counter can't distinguish the connected-goods in the belt of fertilizer company so a new multifunctional and intelligent counter is designed. This counter has some optoelectronic switch and speed measurement module cooperated to classify which case it is in conveyor belt for precise counting. For adapting different operating environment the system can use buttons to modify the system parameters.

A new sensorless speed control method which is based on new sliding-mode observer (SMO) is proposed for permanent-magnet synchronous motor (PMSM). The main basis of the observer is the back electromotive force (EMF). The rotor speed is estimated by the back EMF observer (BEMFO). The method can be better than low-pass filter in accuracy. In the observer, the stator resistance also is as an estimator to improve the robustness of the system. The stability of the system is proved by the Lyapunov theory. And numerical simulations based on M-file in Matlab is put into practice. The simulation results testify that the novel sensorless speed control method based on sliding-mode observer is fast and pinpoint for speed tracking control of PMSM.

This paper proposes a novel method to estimate the state of charge (SOC) based on a new sliding mode observer (SMO) and an equivalent circuit model (ECM) for lithium-ion batteries. A new sliding mode observer is designed to attain robustness in terms of model errors and uncertainties. The new switching function is chosen for chattering reduction. The stability of designed observer is proved with Lyapunov theory. The simulation results show better tracking performance and quick convergence based on the proposed new method.

This paper proposes an Extended State Observer (ESO) to achieve the sensorless position control of permanent-magnet synchronous motor (PMSM). An Extended State Observer (ESO) is built according to the back electromotive force (EMF) model. It uses a nonsmooth2 structure to observe all states of the controlled object and the inside and outside disturbance of the system. Original system can be converted into linear certain system by this. Furthermore, stability and convergence analysis of the closed-loop control are given based on the Lyapunov stability theory. Simulation results verify the asymptotic convergence of ESO.

It is important that balancing the node's energy consumption effectively in wireless sensor network. We propose a novel double cluster-head routing policy based on clustering hierarchy routing. According to balancing the number of neighbor nodes, surplus energy and distance weights, we adopt first and second cluster-head mode. Cluster heads are responsible for data collection and transmission respectively. This mechanism could avoid high energy consumption in single cluster head and “false” death phenomenon. Simulation results show that based on the weights and a periodic cluster-head selection will increase the overall performance of the network, decrease the cluster head energy consumption, balance energy consumption between member nodes, and prolong the entire lifetime of network.

To develop an energy-efficient routing protocol becomes one of the most important and difficult key tasks for wireless sensor networks. Traditional Rumor Routing is effective in random path building but winding or even looped path is prone to be formed, leading to enormous energy wasting. Clustering Rumor Routing proposed in this paper has advantages in energy saving by making full use of three features—clustering structure, selective next-hop scheme and double variable energy thresholds for rotation of cluster-heads. Thus CRR can effectively avoid the problems that occur in RR and a more energy-efficient routing path from event agents to queries can be built. The performance between CRR and traditional RR are evaluated by simulations. The results indicate that compared with traditional RR, the CRR can save more energy consumption, provide better path quality, and improve the delivery rate as well.

the thesis introduces grey system model and BP neural network. Through making full use of the merits of GM(1.1) and neural network model and overcoming their drawbacks, we construct the grey residue amending combined and prediction model based on BP Neural network, and such combined model as "combined prediction model= tendency prediction model/GM(1.1)+neural network model", and makes a contrast between the three models in prediction and precision. The result indicates that, the combined model is better than that of the single models for higher precision and smaller error.

This paper builds the fault diagnosis model and optimizes the input interface of the model by normalizing the initial data of the performance parameter for the automobile engine, carrying on information fusion and adopting the Adaptive Neural Fuzzy Interference System (ANFIS). The recognition rate of the model reaches 94.38% under the test of field test data. Corresponding BP neural network modeling and fuzzy recognition modeling, the model enjoys reliability, strong generalization ability, and high failure recognition rate. Moreover, it can effectively detect the performance parameter failure for the automobile engine.

This article presents a kind of adaptive robust control strategy for ball and beam system to the modeling uncertainty, and the dissipation theory to the bounded disturbances, the system is both strictly dissipative and asymptotically adaptive stable for all admissible uncertainties. The system's asymptotically stability and robustness are proved by using Lyapunov theory and dissipation theory . Simulation results show the effectiveness of the proposed control strategy.

Combined with the technologies of photovoltaic generation (PV) and electronic ballast (EB), low pressure sodium lamp (LPS) is being used widely, which possesses effects of energy saving and environmental protection. Usually, three kinds of production are researched and developed independently, so in practical engineering application, the matching ability of them is not unstable, even cause to equipment damage. These have already limited the application of low pressure sodium lamp in large degree. In order to improve matching ability of three separate technologies, this paper aims at studying and developing the integrated system of PV generation and EB for LPS. The nine design goals are firstly presented for the entire control device, and then the working principles of the whole system are analyzed. Based the UCC2305 HID lamp controller, the main circuits are designed in detail, including the battery charging and discharging circuit, DC/DC boost circuit, LRC primary oscillation circuit, DC/AC full-bridge inverter circuit. The key control technologies of the LPS ignition and the load power control loop are described importantly. Finally, the experiments show that this integrated system performance is stable and has high practical value.

The collaborative learning (CL) focusing on the teacher-student and student-student interaction and cooperation is a beneficial attempt to reforming the present teaching model. An experimental research was conducted to acquire some empirical evidence on CLpsilas effectiveness at the college level. Some CL strategies were exercised in non-major college English teaching (CET) of experimental class and three questionnaires and two tests were designed to check the effectiveness of CL. The result of the research indicates the CL strategies have positive influence on non-major CET and they are exercisable and effective. It can enhance the participation of students in class, increase studentspsila commitment to English study after class, relieve anxiety in study and create a harmonious climate of study, which in turn improve studentspsila grades and interaction in CET.

The paper presents a new statistical name entity recognition algorithm, which does not require the collection and manual annotation of domain-specific sentences to train the models. The models of the name entities are domain-independent and could be directly applied to other domains of applications. This technique can also be applied to decode a set of raw sentences iteratively, if available, and use the decoded output to improve the statistical models. Applied to the mutual fund trading application, this new technique achieves a performance comparable to that using the decision tree model, which is trained from an annotated corpus. Iterative decoding of a set of natural language utterances and training of the general language model decreases the sentence error rate by 11%.

We introduce a new approach, called non-stationary adaptation (NA), to recognize speech under non-stationary adverse environments. Two models are used: one is a speaker-independent hidden Markov model (HMM) for clean speech, the other is an ergodic Markov chain representing the non-stationary adverse environment. Each state in the Markov chain represents one stationary adverse condition and has associated with it an affine transform that is estimated by maximum likelihood linear regression (MLLR). Three kinds of adverse environments are considered: (i) multi-speaker speech recognition where the speaker identity changes randomly and this constitutes a non-stationary adverse condition, (ii) the recognition of speech corrupted by machinegun noise, and (iii) the crosstalk problem. The algorithm is tested on the Nov92 development database of WSJF0 with a vocabulary size of 20000. In multi-speaker speech recognition, NA decreases the error rate by 13.6%. For speech corrupted by machinegun noise, a one-state Markov chain decreases the error rate by 18%, and a two-state Markov chain gives another 14% decrease in error rate. In the crosstalk problem, a one-state Markov chain decreases the error rate by 16.8%. Two-state and three-state Markov chains decrease the error rate by 22% and 24.4%, respectively.

Elliptical curl antennas that can radiated circularly polarized waves are analyzed in detail. The axial ratio of the radiated wave is found to be dependent on the axial ratio of the curled section (physical axial ratio), the end angle of the curled section, the wire radius and the length of the vertical section. A two-element array is constructed. The input impedance and radiation pattern of one single curl element, the radiation pattern of two-element array are calculated and are in good agreements with the measurement.

This paper reports an ongoing effort to develop an unsupervised on-line speaker adaptation method for the telephony environment. All speakers in the training data corpus are acoustically pre-clustered into clusters, and a cluster-dependent system is built for each cluster. When a new telephony test speaker is given, a cluster, which is the closest to the speaker, is determined and selected by an improved distance measure. Based on this selected cluster, a MLLR (maximum likelihood linear regression) adaptation algorithm with block diagonal transformation is applied to move the cluster model to be closer to the testing speaker. For telephony applications the adaptation data can be very short or noisy, potentially the MLLR adapted means can be unreliable. A MAP-like weighting scheme for MLLR adaptation is applied to ensure the adapted mean reliability when the adaptation data is very short.