During nuclear decommissioning, large structures often need to be cut into smaller parts before being packed into containers. Cutting and packing incur costs, and improving efficiency in one often increases costs in the other, requiring a trade-off. Considering the operational costs of different cutting tools and container types, determining the optimal number of parts to cut a structure into, and how to pack these parts efficiently, is challenging. This paper presents a novel voxel-based cutting and packing optimization algorithm designed to minimize overall costs for decommissioning nuclear structures. We define cost as comprising two factors: cutting cost, which depends on the amount of cutting required and the type of tool used, and packing cost, which is determined by the number of containers needed and the unit cost of a container. We demonstrate that our algorithm can successfully minimize overall cutting and packing costs. Additionally, we show how our algorithm can be used to compare different decommissioning scenarios by cutting a simple structure found in nuclear decommissioning and packing the pieces into two different container types with different unit costs. Our results also show that the algorithm outperforms a minimal cutting approach, where a minimum number of cuts is used to segment the structure.
In existing hand gesture recognition research, single-modal recognition is commonly used. For example, visual hand gesture recognition uses image information, but it is easily affected by the shooting environment. Another example is using surface electromyography (sEMG) for recognition, but it is susceptible to signal noise. To address the above issues, this paper focuses on the fusion of sEMG and vision of the human hand. We propose a novel approach that fuses the two modalities by using convolutional neural networks (CNN) to improve recognition accuracy. Firstly, using an RGB camera and sEMG armband, we jointly collect sEMG signal and skeleton in real-time, creating our own multimodal dataset for training. Secondly, we design a multimodal recognition network with feature fusion of sEMG and skeleton, to achieve an increase in accuracy. Finally, we built a human-computer interaction system that realizes hand gestures to manipulate a dexterous hand and a robot arm. Experimental results demonstrate that the fusion of the two modalities has complementary effects and effectively improves recognition accuracy.
Continuous curvilinear capsulorhexis (CCC) is a delicate operation that may benefit from robot technology. This paper introduces a new hybrid cataract surgery robot that consists of four parallel prismatic pairs connected in series to a "Rotation-Prismatic" unit. Additionally, the paper proposes a master-slave control strategy and a virtual remote center of motion (RCM) algorithm. The virtual RCM is based on the kinematic model of the robot and enables the setting of an arbitrary point on the forceps as the RCM point. Finally, the effectiveness of the proposed robot is verified through experimental evaluations on ex-vivo pig eyeballs.
Knee osteoarthritis (KOA) is a major cause of morbidity, especially in the older and female population. Automatic medical image segmentation is crucial for improving the diagnostic accuracy of KOA and optimizing treatment plans. Convolutional neural networks have evinced potential in enhancing the accuracy of KOA segmentation. However, CNNs face challenges in handling the complex variability in position, shape, and scale of segmentation targets. Additionally, existing CNN models struggle with accurate processing boundary information, which limits their effectiveness in clinical decision-making. To tackle these challenges, we introduce a deep-learning architecture known as STRR-Net, designed for the automated segmentation of knee joints in MRI scans. We also designed a novel parallel encoder structure to achieve complementary and enhanced feature extraction. In particular, we propose a skip-connection structure for multi-scale fusion, which allows us to simultaneously capture different levels of feature information. Our model attains an average Dice of 0.8176 for the knee dataset, with a score of 0.6653 for cartilage segmentation and 0.8320 for meniscus segmentation. Visualizing the feature maps allows for a detailed assessment of the knee joint’s anatomy, including cartilage thickness, thereby facilitating the timely identification of osteoarthritis.
The shield tunneling method is currently the most widely used underground construction technique, where the formed tunnel is composed of many sequentially arranged segments. Before construction, a preliminary layout of the design tunnel axis (DTA) is required to assess the segments' ability to fit the DTA and determine the number of segments needed. Existing preliminary layout methods only consider the axis fitting deviation of the next ring of segments to select the assembly point, which can lead to significantly increased axis deviations for subsequent segments, potentially exceeding allowable limits. This results in incorrect assessments of the segments' fitting ability, particularly for DTAs with small curvature radius. To address this issue, this paper proposes a segment preliminary layout method that considers the maximum fitting deviation of the next multiple rings of segments. The method uses a differential evolution algorithm to optimize the assembly point combinations of the next multiple segment rings. It employs a segment pose transfer algorithm to calculate the poses of the next multiple rings, and then computes the corresponding axis fitting deviations for these rings. The assembly point combinations with the minimum maximum axis fitting deviation is selected as the optimal solution, and the first assembly point is selected to assembly next segment. This method was applied to a 900m radius circular arc DTA using 28-point large diameter segments with a calculation of five segment rings. The maximum axis fitting deviation across the entire DTA was 9.3 mm, significantly lower than the 52.8 mm deviation obtained using existing layout methods.
