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This study aims to solve the problem of the existing metal foreign object (MFO) detecting systems, which are not sensitive to the small size MFO in wireless charging region of electric vehicle (EV) because of the extremely complex signal noise in the process of wireless charging of EV.

A method for MFO detection based on the principle that MFOs can cause mistuned resonance of detection coil resonant circuit is proposed. The general scheme of detecting system is proposed. The design method for detection coils which is effective to small MFOs detection in large-area region of wireless charging of EV is presented. The design of time-sharing driving circuit and amplifying circuit of high frequency exciting signal for detection coils is introduced. The design scheme of signal processing circuit (including filter and rectifier) of detection coil terminal voltage is also proposed.

The influence of exciting frequency of detection coils on detecting sensitivity and the anti-noise feature of system are analyzed according to the experiment results.

The experiment of MFO detection indicates that the proposed method can effectively detect the coin-sized small MFO in the process of wireless charging of EV.

We propose herein a motion detection artificial vision model which uses analog electronic circuits. The proposed model is comprised of four layers. The first layer is a differentiation circuit of the large capacitor and resistance (CR) coefficient, and the second layer is a differentiation circuit of the small CR coefficient. Thus, the speed of the movement object is detected. The third layer is a difference circuit for detecting the movement direction, and the fourth layer is a multiple circuit for detecting pure motion output. The model was shown to be capable of detecting a movement object in the image. Moreover, the proposed model can be used to detect two or more objects, which is advantageous for detection in an environment in which several objects are moving in multiple directions simultaneously. From a technological viewpoint, the proposed model facilitates clarification of the mechanism of the biomedical vision system, which should enable design and simulation by an analog electric circuit for detecting the movement and speed of objects.

This paper aims to propose an integrated protection scheme for converters of a low-power, low-cost photovoltaic system. Power electronic converters use a variety of methods to limit overload and fault current. The use of insulated and non-insulated sensors along with additional circuits to detect and limit fault current can cause current to be limited or completely cut off before damage to semiconductor devices. In addition, fuses that have slower performance are used as backup for any type of protection.

First, all the candidate points for protection are investigated. In this paper, after examining the performance of glass fuses as linear resistors, they are used as a current feedback element. A simple, isolated and reliable circuit for fault detection at various points of the system has been proposed that can be implemented and operated in single shot or auto-reclose operating mode.

The experimental results of this circuit on a dc/dc converter and an H-bridge inverter show that it can cut off all instantaneous short circuit errors in less than 50 µs and prevent damage to the semiconductor switch.

In low-cost and low-power converters, it is usually not cost-effective to use complex and expensive devices. For this reason, these converters are more vulnerable to faults. On the other hand, in complex systems such as photovoltaics, several converters are used simultaneously in different parts, and the occurrence of a fault in each of them causes the whole system to fail.

The purpose of this paper is to improve photoelectric detection target (PDT) optical detection performance and detection view, by analyzing its influence factors and putting forward a new method to design its optical detection system.

Using rectangle linked photoelectric detector, with low noise and high response, to design optical detection system and gain faint projectile image information; bringing forward a deviating focusing technique to eliminate detection blind area of photoelectric detector; and designing adjustable slit diaphragm to weaken background light influence.

The results of experimentation in shooting range show that the new PDT has improved detection sensitivity and performance.

The paper presents a new design method in photoelectric detection target (PDT) optical detection system, which can provide a new method to design fire across measurement system and gain accurate projectile's coordinates data in the shooting range.

The purpose of this paper is to present a detection method based on computer vision for automatic flexible printed circuit (FPC) defect detection.

This paper proposes a new method of watershed segmentation based on morphology. A dimensional increment matrix calculation method and an image segmentation method combined with a fuzzy clustering algorithm are provided. The visibility of the segmented image and the segmentation accuracy of a defective image are guaranteed.

Compared with the traditional one, the segmentation result obtained in this study is superior in aspects of noise control and defect segmentation. It completely proves that the segmentation method proposed in this study is better matches the requirements of FPC defect extraction and can more effectively provide the segmentation result. Compared with traditional human operators, this system ensures greater accuracy and more objective detection results.

The extraction of FPC defect characteristics contains some obvious characteristics as well as many implied characteristics. These characteristics can be extracted through specific space conversion and arithmetical operation. Therefore, more images are required for analysis and foresight to establish a more widely used FPC defect detection sorting algorithm.

This paper proposes a new method of watershed segmentation based on morphology. It combines a traditional edge detection algorithm and mathematical morphology. The FPC surface defect detection system can meet the requirements of online detection through constant design and improvement. Therefore, human operators will be replaced by machine vision, which can preferably reduce the production costs and improve the efficiency of FPC production.

This paper provides an effective study to detect and locate the inter-turn short-circuit faults (ITSC) in a three-phase induction motor (IM) using the support vector machine (SVM). The characteristics extracted from the analysis of the phase shifts between the stator currents and their corresponding voltages are used as inputs to train the SVM. The latter automatically decides on the IM state, either a healthy motor or a short-circuit fault on one of its three phases.

To evaluate the performance of the SVM, three supervised algorithms of machine learning, namely, multi-layer perceptron neural networks (MLPNNs), radial basis function neural networks (RBFNNs) and extreme learning machine (ELM) are used along with the SVM in this study. Thus, all classifiers (SVM, MLPNN, RBFNN and ELM) are tested and the results are compared with the same data set.

The obtained results showed that the SVM outperforms MLPNN, RBFNNs and ELM to diagnose the health status of the IM. Especially, this technique (SVM) provides an excellent performance because it is able to detect a fault of two short-circuited turns (early detection) when the IM is operating under a low load.

The original of this work is to use the SVM algorithm based on the phase shift between the stator currents and their voltages as inputs to detect and locate the ITSC fault.

Flow measurement plays an important role in modern industrial production. Flow measurement methods based on optical fiber systems have become a main research focus.

Applying flow measurement theory and the structural characteristics of optical fiber, flow measurement of paste can be achieved through a combined laser Doppler and optical fiber sensing system based on the principle of optical fiber grating sensors and flow sensors. The system is developed to include light selection, photoelectric detection, a voltage amplifier circuit and a signal filtering circuit.

The system is shown, through a comparative experiment, to be of higher accuracy than the traditional ultrasonic method.

A new method for measuring the paste flow is presented, which is based on the principle of optical fiber Doppler. The method has the advantages of convenient installation, high accuracy and low cost. Experiments show that the method is feasible.

The purpose of this paper is to propose a defect detection method of silicone caps positional deviation on flexible printed circuit board (FPCB) of keyboard based on automatic optical inspection.

First, the center of silicone caps of target keyboard FPCB image was extracted as feature points for generating the feature image which is used for registration rigidly with the reference feature image generated from the CAD drawings. Then, a flexible image registration method based on the surrounding-control-center B-splines (SCCB) strategy was proposed, which could correct the flexible deformation of the image generated by FPCB substrate while keeping the pasting deviation information about silicone caps unchanged. Finally, on this basis, a nearest neighbor strategy was proposed to detect the positional deviation of silicone caps.

Experimental results show that the proposed method can effectively detect the positional deviation defect of silicone caps. The G-mean value of the proposed method is 0.941746, which is 0.3 higher compared to that of similar research.

This paper presents a method to detect positional deviation defect of silicone caps on keyboard FPCB. Different from the classic B-spline image registration method, the proposed SCCB method used the neighborhood information of the pixel to be registered selectively to calculate the displacement vector needed for its registration, which overcame the problem that the silicone cap pasting deviation information disappears with the correction of the flexible deformation of the image.

The purpose of this paper is to design a low-power human physiological parameters monitoring system which can monitor six vital parameters simultaneously based on wearable body sensor network.

This paper presents a low-power multiple physiological parameters monitoring system (MPMS) which comprises four subsystems. These are: electrocardiogram (ECG)/respiration (RESP) parameters monitoring subsystem with embedded algorithms; blood oxygen (SpO₂)/pulse rate (PR)/body temperature (BT)/blood pressure (BP) parameters monitoring subsystem with embedded algorithms; main control subsystem which is in charge of system-level power management, communication and interaction design; and upper computer software subsystem which manipulates system function and analyzes data.

Results have successfully demonstrated monitoring human ECG, RESP, PR, SpO₂, BP and BT simultaneously using the MPMS device. In addition, the power reduction technique developed in this work at the physical/hardware level is effective. Reliability of algorithms developed for monitoring these parameters is assessed by Fluke Prosim8 Vital Signs Simulators (produced by Fluke Corp. USA).

The MPMS device provides long-term health monitoring without interference from normal personal activities, which potentially allows applications in real-time daily healthcare monitoring, chronic diseases monitoring, elderly monitoring, human emotions recognization and so on.

First, a power reduction technique at the physical/hardware level is designed to realize low power consumption. Second, the proposed MPMS device enables simultaneously monitoring six key parameters. Third, unlike most monitoring systems in bulk size, the proposed system is much smaller (118 × 58 × 18.5 mm3, 140 g total weight). In addition, a comfortable smart shirt is fabricated to accommodate the portable device, offering reliable measurements.

This paper aims to propose a conceptual scale model of mobile drilling robot according to the actual drilling rig and working conditions to improve the safety and automation of drilling in tunnel construction and coal mining applications.

A couple of pinion and rack serves as the support mechanism driven by a motor with low rotation speed at high power, and these components are assembled in the center of the robot to tightly fasten the whole body together. The drilling rod and the sleeve are connected through a hole with screw thread so that the rod feeds and rotates simultaneously along with the sleeve. The robot model is automatically controlled by a single-chip microcomputer, and the anti-disturbance circuit is designed as well. A five-step rule obstacle avoidance method is proposed to ensure safe and reliable movement.

The results of simulation experiments on drilling operation do indicate that the mechanism and control method are feasible and effective.

The robot is nearly complete but indeed remains only an experimental machine.

The design of the mechanism structure for the conceptual robot is novelty. The method of five-step rule obstacle avoidance can improve reliability of obstacle avoidance according to the experimental results, which can meet the requirements of complex working conditions underground coal mine.