DEVELOPMENT OF A MATHEMATICAL MODEL OF THE FABRIC FINISHING PROCESS

In the article considered the process of dosage of chemicals for bleaching of tissues as a linear dynamic object. Presented a mathematical model based on approximation methods.

Dynamic Object Detection Algorithm Based on Lightweight Shared Feature Pyramid

Current object detection algorithms perform inference on all samples at a fixed computational cost in the inference stage, which wastes computing resources and is not flexible. To solve this problem, a dynamic object detection algorithm based on a lightweight shared feature pyramid is proposed, which performs adaptive inference according to computing resources and the difficulty of samples, greatly improving the efficiency of inference. Specifically, a lightweight shared feature pyramid network and lightweight detection head is proposed to reduce the amount of computation and parameters in the feature fusion part and detection head of the dynamic object detection model. On the PASCAL VOC dataset, under the two conditions of “anytime prediction” and “budgeted batch object detection”, the performance, computation amount and parameter amount are better than the dynamic object detection models constructed by networks such as ResNet, DenseNet and MSDNet.

PyComm: Malicious commands detection model for python scripts

Python is a concise language which can be used to build lightweight tools or dynamic object-orientated applications. The various attributes of Python have made it attractive to numerous malware authors. Attackers often embed malicious shell commands into Python scripts for illegal operations. However, traditional static analysis methods are not feasible to detect this kind of attack because they focus on common features and failure in finding those malicious commands. On the other hand, dynamic analysis is not optimal in this case for its time-consuming and inefficient. In this paper, we propose PyComm, a model for detecting malicious commands in Python scripts with multidimensional features based on machine learning, which considers both 12 statistical features and string sequences of Python source code. Meanwhile, three comparison experiments are designed to evaluate the validity of proposed method. Experimental results show that presented model has achieved an excellent performance based on those practical features and random forest (RF) algorithm, obtained an accuracy of 0.955 with a recall of 0.943.

Following A Dynamic Object Through A Transient Response Adjustable MPC (Extended Version)

We developed an MPC motion controller for a mobile robot to follow a dynamic object. The main contribution is to find an adjustable transient response characteristic in a special formulation of MPC and apply to a real robot platform.

LiDAR Odometry and Mapping Based on Semantic Information for Outdoor Environment

Simultaneous Localization and Mapping (SLAM) in an unknown environment is a crucial part for intelligent mobile robots to achieve high-level navigation and interaction tasks. As one of the typical LiDAR-based SLAM algorithms, the Lidar Odometry and Mapping in Real-time (LOAM) algorithm has shown impressive results. However, LOAM only uses low-level geometric features without considering semantic information. Moreover, the lack of a dynamic object removal strategy limits the algorithm to obtain higher accuracy. To this end, this paper extends the LOAM pipeline by integrating semantic information into the original framework. Specifically, we first propose a two-step dynamic objects filtering strategy. Point-wise semantic labels are then used to improve feature extraction and searching for corresponding points. We evaluate the performance of the proposed method in many challenging scenarios, including highway, country and urban from the KITTI dataset. The results demonstrate that the proposed SLAM system outperforms the state-of-the-art SLAM methods in terms of accuracy and robustness.