scholarly journals A Novel Metal Foreign Object Detection for Wireless High-Power Transfer Using a Two-Layer Balanced Coil Array with a Serial-Resonance Maxwell Bridge

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2070
Author(s):  
Sunhee Kim ◽  
Haeyong Jung ◽  
Youngjun Ju ◽  
Yongseok Lim
Keyword(s):  
Object Detection ◽  
High Power ◽  
Bridge Circuit ◽  
Detection System ◽  
Transmission Efficiency ◽  
Foreign Object ◽  
Power Transfer ◽  
Coil Array ◽  
Foreign Objects ◽  
Change Detection Performance

In a wireless high-power transfer system with a distance of several tens of centimeters apart between the transmitter and receiver coils, one of the most challenging issues is to detect metallic foreign objects between the transmitter and receiver coils. The metallic foreign objects must be detected and removed since these reduce the transmission efficiency and cause heat generation of the transmitter and receiver. This paper presents two-layer symmetric balanced coil array so that if there are metallic foreign objects, it can be detected through the change of the inductance of the balanced coils. Since the balanced coil is composed of coils that are in a symmetrical relationship in position, there is no need for a reference coil, and interference between coils is reduced by dividing the coil into two layers. In addition, a novel serial-resonance Maxwell bridge circuit to improve the inductance change detection performance is presented in this paper. The proposed metallic foreign object detection system is implemented using two-layer balanced coil array with a serial-resonance Maxwell bridge and the experimental results show that voltage changes of hundreds of mV to several V occur when a metallic foreign object is inserted, so that even small metals such as clips can be detected.

scholarly journals Rapid Foreign Object Detection System on Seaweed Using VNIR Hyperspectral Imaging

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5279
Author(s):  
Dong-Hoon Kwak ◽  
Guk-Jin Son ◽  
Mi-Kyung Park ◽  
Young-Duk Kim
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Hyperspectral Imaging ◽  
High Speed ◽  
Detection System ◽  
Imaging Techniques ◽  
Foreign Object ◽  
Subtraction Method ◽  
Foreign Objects ◽  
Time Operation

The consumption of seaweed is increasing year by year worldwide. Therefore, the foreign object inspection of seaweed is becoming increasingly important. Seaweed is mixed with various materials such as laver and sargassum fusiforme. So it has various colors even in the same seaweed. In addition, the surface is uneven and greasy, causing diffuse reflections frequently. For these reasons, it is difficult to detect foreign objects in seaweed, so the accuracy of conventional foreign object detectors used in real manufacturing sites is less than 80%. Supporting real-time inspection should also be considered when inspecting foreign objects. Since seaweed requires mass production, rapid inspection is essential. However, hyperspectral imaging techniques are generally not suitable for high-speed inspection. In this study, we overcome this limitation by using dimensionality reduction and using simplified operations. For accuracy improvement, the proposed algorithm is carried out in 2 stages. Firstly, the subtraction method is used to clearly distinguish seaweed and conveyor belts, and also detect some relatively easy to detect foreign objects. Secondly, a standardization inspection is performed based on the result of the subtraction method. During this process, the proposed scheme adopts simplified and burdenless calculations such as subtraction, division, and one-by-one matching, which achieves both accuracy and low latency performance. In the experiment to evaluate the performance, 60 normal seaweeds and 60 seaweeds containing foreign objects were used, and the accuracy of the proposed algorithm is 95%. Finally, by implementing the proposed algorithm as a foreign object detection platform, it was confirmed that real-time operation in rapid inspection was possible, and the possibility of deployment in real manufacturing sites was confirmed.

scholarly journals Unsupervised Foreign Object Detection Based on Dual-Energy Absorptiometry in the Food Industry

2021 ◽  
Vol 7 (7) ◽  
pp. 104
Author(s):  
Vladyslav Andriiashen ◽  
Robert van Liere ◽  
Tristan van Leeuwen ◽  
Kees Joost Batenburg
Keyword(s):  
Object Detection ◽  
Meat Products ◽  
Food Product ◽  
Processing Technique ◽  
Dual Energy ◽  
Foreign Object ◽  
Metal Debris ◽  
X Ray ◽  
Foreign Objects ◽  
X Ray Imaging

X-ray imaging is a widely used technique for non-destructive inspection of agricultural food products. One application of X-ray imaging is the autonomous, in-line detection of foreign objects in food samples. Examples of such inclusions are bone fragments in meat products, plastic and metal debris in fish, and fruit infestations. This article presents a processing methodology for unsupervised foreign object detection based on dual-energy X-ray absorptiometry (DEXA). A novel thickness correction model is introduced as a pre-processing technique for DEXA data. The aim of the model is to homogenize regions in the image that belong to the food product and to enhance contrast where the foreign object is present. In this way, the segmentation of the foreign object is more robust to noise and lack of contrast. The proposed methodology was applied to a dataset of 488 samples of meat products acquired from a conveyor belt. Approximately 60% of the samples contain foreign objects of different types and sizes, while the rest of the samples are void of foreign objects. The results show that samples without foreign objects are correctly identified in 97% of cases and that the overall accuracy of foreign object detection reaches 95%.

Foreign Object Detection for Wireless Power Transfer

2018 ◽  
Author(s):  
Lingxin Lan ◽  
Nicholas M. Ting ◽  
Samer Aldhaher ◽  
George Kkelis ◽  
Christopher H. Kwan ◽  
...  
Keyword(s):  
Object Detection ◽  
Wireless Power Transfer ◽  
Foreign Object ◽  
Power Transfer ◽  
Wireless Power

scholarly journals Metal Object Detection in a Wireless High-Power Transfer System Using Phase–Magnitude Variation

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2952
Author(s):  
Sunhee Kim ◽  
Woong Choi ◽  
Yongseok Lim
Keyword(s):  
High Power ◽  
Moving Objects ◽  
Detection System ◽  
Wireless Power Transfer ◽  
Transfer System ◽  
Power Transfer ◽  
Wireless Power ◽  
Metal Object ◽  
Fire Hazards ◽  
Detection Coil

Recently, wireless charging technologies for large moving objects, such as electric vehicles and robots, have been actively researched. The power transmitting and receiving coils in most large moving objects are structurally separated by a given distance, which exposes a high output power to the outside world. If a foreign metal object enters the area between these two coils during wireless power transfer, fire hazards or equipment damage may occur. Therefore, we propose a method for detecting foreign metal objects in the gap between the transmitting and receiving coils in a wireless high-power transfer system. A resonant detection coil set is used to exploit the change induced in electrical characteristics when a foreign metal object is inserted. The mutual inductance of the foreign metal object changes the impedance of the detection coil set. We developed a simple circuit to detect both the magnitude and phase change of the voltage signal according to the altered impedance. Additionally, we implemented a prototype of a wireless power transfer system with a detection system to verify that even small foreign metal objects can be detected effectively.

scholarly journals U-Net-Based Foreign Object Detection Method Using Effective Image Acquisition System: A Case of Almond and Green Onion Flake Food Process

2021 ◽  
Vol 13 (24) ◽  
pp. 13834
Author(s):  
Guk-Jin Son ◽  
Dong-Hoon Kwak ◽  
Mi-Kyung Park ◽  
Young-Duk Kim ◽  
Hee-Chul Jung
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Quality Evaluation ◽  
Detection Algorithm ◽  
Synthetic Dataset ◽  
Foreign Object ◽  
Food Manufacturing ◽  
Real Dataset ◽  
The Real ◽  
Foreign Objects

Supervised deep learning-based foreign object detection algorithms are tedious, costly, and time-consuming because they usually require a large number of training datasets and annotations. These disadvantages make them frequently unsuitable for food quality evaluation and food manufacturing processes. However, the deep learning-based foreign object detection algorithm is an effective method to overcome the disadvantages of conventional foreign object detection methods mainly used in food inspection. For example, color sorter machines cannot detect foreign objects with a color similar to food, and the performance is easily degraded by changes in illuminance. Therefore, to detect foreign objects, we use a deep learning-based foreign object detection algorithm (model). In this paper, we present a synthetic method to efficiently acquire a training dataset of deep learning that can be used for food quality evaluation and food manufacturing processes. Moreover, we perform data augmentation using color jitter on a synthetic dataset and show that this approach significantly improves the illumination invariance features of the model trained on synthetic datasets. The F1-score of the model that trained the synthetic dataset of almonds at 360 lux illumination intensity achieved a performance of 0.82, similar to the F1-score of the model that trained the real dataset. Moreover, the F1-score of the model trained with the real dataset combined with the synthetic dataset achieved better performance than the model trained with the real dataset in the change of illumination. In addition, compared with the traditional method of using color sorter machines to detect foreign objects, the model trained on the synthetic dataset has obvious advantages in accuracy and efficiency. These results indicate that the synthetic dataset not only competes with the real dataset, but they also complement each other.

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