After each robot end tool replacement, tool center point (TCP) calibration must be performed to achieve precise control of the end tool. This process is also essential for robot-assisted puncture surgery. The purpose of this article is to solve the problems of poor accuracy stability and strong operational dependence in traditional TCP calibration methods and to propose a TCP calibration method that is more suitable for a physician. This paper designs a special binocular vision system and proposes a vision-based TCP calibration algorithm that simultaneously identifies tool center point position (TCPP) and tool center point frame (TCPF). An accuracy test experiment proves that the designed special binocular system has a positioning accuracy of ±0.05 mm. Experimental research shows that the magnitude of the robot configuration set is a key factor affecting the accuracy of TCPP. Accuracy of TCPF is not sensitive to the robot configuration set. Comparison experiments show that the proposed TCP calibration method reduces the time consumption by 82%, improves the accuracy of TCPP by 65% and improves the accuracy of TCPF by 52% compared to the traditional method. Therefore, the method proposed in this article has higher accuracy, better stability, less time consumption and less dependence on the operations than traditional methods, which has a positive effect on the clinical application of high-precision robot-assisted puncture surgery.
Malicious webshells currently present tremendous threats to cloud security. Most relevant studies and open webshell datasets consider malicious webshell defense as a binary classification problem, that is, identifying whether a webshell is malicious or benign. However, a fine-grained multi-classification is urgently needed to enable precise responses and active defenses on malicious webshell threats. This paper introduces a malicious webshell family dataset named MWF to facilitate webshell multi-classification researches. This dataset contains 1,359 malicious webshell samples originally obtained from the cloud servers of Alibaba Cloud. Each of them is provided with a family label. The samples of the same family generally present similar characteristics or behaviors. The dataset has a total of 78 families and 22 outliers. Moreover, this paper introduces the human-machine collaboration process that is adopted to remove benign or duplicate samples, address privacy issues, and determine the family of each sample. This paper also compares the distinguished features of the MWF dataset with previous datasets and summarizes the potential applied areas in cloud security and generalized sequence, graph, and tree data analytics and visualization.
Purpose With the rapid development of the 3C industry, the problem of automated operation of 3C wire is becoming increasingly prominent. However, the 3C wire has high flexibility, and its deformation is difficult to model and control. How to realize the automation operation of flexible wire in 3C products is still an important issue that restricts the development of the 3C industry. Therefore, this paper designs a system that aims to improve the automation level of the 3C industry. Design/methodology/approach This paper designed a visual servo control system. Based on the perception of the flexible wire, a Jacobi matrix is used to relate the deformation of the wire to the action of the robot end; by building and optimizing the Jacobi matrix, the robot can control the flexible wire. Findings By using the visual servo control system, the shape and deformation of the flexible wire are perceived, and based on this, the robot can control the deformation of the flexible wire well. The experimental environment was built to evaluate the accuracy and stability of the system for controlling the deformation of the flexible wire. Originality/value An image-based visual servo system is proposed to operate the flexible wire, including the vision system, visual controller and joint velocity controller. It is a scheme suitable for flexible wire operation, which has helped to automate flexible wire-related industries. Its core is to correlate the motion of the robot end with the deformation of the flexible wire through the Jacobian matrix.
The validity of brachytherapy has been proved in clinical tumor treatment, and robotic surgery system has been used in recent years to ensure surgical consistency. In our work, a new automatic radioactive particle implantation device with two degrees of freedom was proposed to apply in the brachytherapy robotic surgery system as an end effector. Furthermore, physical assembling and simple function verifying experiments were accomplished. This device can help simplify the operating steps during surgery, and improve the stability of the surgical system, and enhances the surgical efficiency.
The demands for digital pathology systems have increased dramatically in the last decade as Virtual Microscopy (VM) has gained increasing popularity. Many digital slide acquisition systems have been developed to meet this demand, utilising a variety of image scan techniques. However, the requirements for, and performance of, these scan techniques are largely undocumented. Therefore, in this paper we evaluate the three primary approaches to digital slide scanning in light field microscopy: field-of-view (FOV) scan, line scan and slanted specimen scan. Initially, we develop equations for each technique that estimates their theoretical scan times in terms data throughput rates. Next, we compare each system's performance based on the relationships between illumination, camera frame rates, data transfer rates and microscope stage speed. We conclude that slanted scan system capable of acquiring multiple focal planes in one pass have the potential to obtain the shortest scan times within current constraints on stage and camera hardware.
Rapid and high-quality digital acquisition of microscopic specimens is critically dependent upon an initial low-resolution delineation of the specimen and the construction of a focus map to guide the subsequent high-resolution scan. In this paper, we propose a novel field-of-view (FOV) based algorithm that not only accurately delineates the specimen, but also highlights superior FOVs from which to build the focus map. Specifically, we evaluate two FOV evaluation metrics: one "baseline" method based on a threshold of intensity values in each FOV and the other based on the proposed auto-phase correlation index (APCI). While phase correlation is a well known measure of phase diversity, we show that APCI can be efficiently utilised as a metric to both delineate the specimen and highlight superior FOV focus candidates. The experimental results, on real-world cytology specimens, demonstrate that APCI is both effective and robust at specimen delineation, particularly in the presence of artifacts such as glue and dust. In addition, APCI is shown to be effective at selecting a subset of FOVs that can be correctly focused.
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