Due to increasingly serious environmental issues, there are growing concerns about the environmental impact of automated container terminals (ACTs). Therefore, the ACT needs to strike a balance between effectiveness and environmental sustainability. AGV scheduling is a vital issue that plays an important role in the efficiency improvement and energy conservation of ACTs. This study examines the AGV scheduling issue in ACTs while taking energy usage and AGV efficiency into account. First, a multi-objective mixed integer programming model with the goals of energy consumption and makespan is built. Then, an improved whale optimization algorithm (IWOA) is proposed to solve this problem, in which task combination and adaptive parameter adjustment strategies are presented to enhance the algorithm. Finally, case study examples are conducted to verify the effectiveness of the proposed method.
With the development of intelligent vehicle technology, the environment perception of intelligent vehicles is becoming more and more important. The detection of pavement roughness is an important component of environmental perception. In this paper, binocular stereo vision technology is used to detect the roughness of pavement on which vehicles drive, and a square segmentation method is used to calculate the value of the roughness. The binocular vision sensor is used to acquire the image and process it to get the depth image. Then square segmentation method is used to calculate the roughness of the pavement. By comparing scanning each point individually and calculating the difference of each point, square segmentation method will greatly reduce the calculating time. Through the algorithm verification of the actual pavement detection, the experiment shows the effectiveness of the method.
Edge computing is an extension of the cloud computing paradigm that shifts part of computing data, applications and services from the cloud server to the network edge, providing low-latency, mobility and location-aware support for delaysensitive applications. The elevators in the high-rise buildings are geographically distributed and movable. Safety and reliability of elevators have attracted people’s attention. Security problem in the elevator is a key issue, especially in emergencies requiring fast response and low latency. In this paper, an elevator abnormal behavior video surveillance system is designed and developed using edge computing paradigm. The recognition of abnormal image sequences and the evaluation of abnormal behavior are realized. Collecting, processing, and analyzing video images are completed at the network edge in real time. The Edge computing nodes are distributed and deployed according to the geographic location of the elevator. The edge nodes are based on mobile embedded devices, and use the computing resources of the embedded devices to implement edge computing at the network edge. Through the edge network, there are several edge nodes based clusters being built to perform distributed computation tasks.
Due to the advantages of having large storage capacity and small code area, QR (quick response) codes have been widely used for automatic identification in many commercial applications such as parcel packaging, business cards and etc. The existing methods mainly focus on unambiguous QR code location with simple background, which always rely on the accomplishment of machine independently. While the QR code images with low quality and complex background always affect the accuracy and efficiency of location in automatic identification, especially the QR code images in which the finder patterns are destroyed. With the help of human, many interactive learning approaches can solve the problem of cognitive obstacles in computer operations. This paper focuses on locating blur QR codes with complex background by an efficient interactive two-stage framework. The first stage is rough location, which includes our interactive feature template setting and clustering process with our improved mean shift algorithm. Then we do the accurate location based on the optimization of the finder pattern detection. Experiments are performed on damaged, contaminated and scratched images with a complex background, which provide a quite promising result for QR code location.
The order of cigarette market is a key issue in the tobacco business system. The anti-counterfeiting code, as a kind of effective anti-counterfeiting technology, can identify counterfeit goods, and effectively maintain the normal order of market and consumers' rights and interests. There are complex backgrounds, light interference and other problems in the anti-counterfeiting code images obtained by the tobacco recognizer. To solve these problems, the paper proposes a locating method based on Susan operator, combined with sliding window and line scanning,. In order to reduce the interference of background and noise, we extract the red component of the image and convert the color image into gray image. For the confusing characters, recognition results correction based on the template matching method has been adopted to improve the recognition rate. In this method, the anti-counterfeiting code can be located and recognized correctly in the image with complex background. The experiment results show the effectiveness and feasibility of the approach.
Automatic image annotation is now a tough task in computer vision, the main sense of this tech is to deal with managing the massive image on the Internet and assisting intelligent retrieval. This paper designs a new image annotation model based on visual bag of words, using the low level features like color and texture information as well as mid-level feature as SIFT, and mixture the pic2pic, label2pic and label2label correlation to measure the correlation degree of labels and images. We aim to prune the specific features for each single label and formalize the annotation task as a learning process base on Positive-Negative Instances Learning. Experiments are performed using the Corel5K Dataset, and provide a quite promising result when comparing with other existing methods.
With the development of barcodes for commercial use, people’s requirements for detecting barcodes by smart phone become increasingly pressing. The low quality of barcode image captured by mobile phone always affects the decoding and recognition rates. This paper focuses on locating and decoding EAN-13 barcodes in fuzzy images. We present a more accurate locating algorithm based on segment length and high fault-tolerant rate algorithm for decoding barcodes. Unlike existing approaches, location algorithm is based on the edge segment length of EAN -13 barcodes, while our decoding algorithm allows the appearance of fuzzy region in barcode image. Experimental results are performed on damaged, contaminated and scratched digital images, and provide a quite promising result for EAN -13 barcode location and decoding.
This paper presents a spiral bevel gear strain measurement using optical fiber gratings. High-speed and heavy-duty spiral bevel gear (SBG) is the key component of the power transmission of intersection axes. Its dynamic mechanical properties greatly influence the working performance of the machine. By building a strain detecting system based on FBG demodulation, we carried out real-time measurements of the distributed strain in the SBG with different torques and different rotation speed. The experimental results show a complete strain waveform from gear-in to gear-out, verifying the feasibility of measuring the strain of SBG using optical fiber gratings.
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