Design and Analysis of a Sensor System for Cutting Force Measurement in Machining Processes.

Multi-component force sensors have infiltrated a wide variety of automation products since the 1970s. However, one seldom finds full-component sensor systems available in the market for cutting force measurement in machine processes. In this paper, a new six-component sensor system with a compact monolithic elastic element (EE) is designed and developed to detect the tangential cutting forces Fx, Fy and Fz (i.e., forces along x-, y-, and z-axis) as well as the cutting moments Mx, My and Mz (i.e., moments about x-, y-, and z-axis) simultaneously. Optimal structural parameters of the EE are carefully designed via simulation-driven optimization. Moreover, a prototype sensor system is fabricated, which is applied to a 5-axis parallel kinematic machining center. Calibration experimental results demonstrate that the system is capable of measuring cutting forces and moments with good linearity while minimizing coupling error. Both the Finite Element Analysis (FEA) and calibration experimental studies validate the high performance of the proposed sensor system that is expected to be adopted into machining processes.

Multi-proportion channel ensemble model for retinal vessel segmentation.

OBJECTIVE: A novel supervised method that is based on the Multi-Proportion Channel Ensemble Model (MPC-EM) is proposed to obtain more vessel details with reduced computational complexity. METHODS: Existing Retinal Vessel Segmentation (RVS) algorithms only work using the single G channel (Green Channel) of fundus images because that channel normally contains the most details with the least noise, while the red and blue channels are usually saturated and noisy. However, we find that the images that are composed of the αG-channel and (1-α) R-channel (Red Channel) with different values of α produce multiple particular global features. This enables the model to detect more local vessel details in fundus images. Therefore, we provide a detailed description and evaluation of the segmentation approach based on the MPC-EM for the RVS. The segmentation approach consists of five identical submodels. Each submodel can capture various vessel details by being trained using different composition images. These probabilistic maps that are produced by five submodels are averaged to achieve the final refined segmentation results. RESULTS: The proposed approach is evaluated using 4 well-established datasets, i.e., DRIVE, STARE, HRF and CHASE_DB1, with accuracies of 95.74%, 96.95%, 96.31%, and 96.54%, respectively. Additionally, quantitative comparisons with other existing methods and cross-training results are included. CONCLUSION: The segmentation results showed that the proposed algorithm based on the MPC-EM with simple submodels can achieve state-of-the-art accuracy with reduced computational complexity. SIGNIFICANCE: Compared with other existing methods that are trained using only the G channel and raw images, the proposed approach based on the MPC-EM, submodels of which are trained using different proportional compositions of R and G channels, obtains better segmentation accuracy and robustness. Additionally, the experimental results show that the R channel of fundus images can also produce performance gains for RVS.

AMC-Net: Asymmetric and multi-scale convolutional neural network for multi-label HPA classification.

BACKGROUND AND OBJECTIVES: The multi-label Human Protein Atlas (HPA) classification can yield a better understanding of human diseases and help doctors to enhance the automatic analysis of biomedical images. The existing automatic protein recognition methods have been limited to single pattern. Therefore, an automatic multi-label human protein atlas recognition system with satisfactory performance should be conducted. This work aims to build an automatic recognition system for multi-label human protein atlas classification based on deep learning. METHODS: In this work, an automatic feature extraction and multi-label classification framework is proposed. Specifically, an asymmetric and multi-scale convolutional neural network is designed for HPA classification. Furthermore, this work introduces a combined loss that consists of the binary cross-entropy and F1-score losses to improve identification performance. RESULTS: Rigorous experiments are conducted to estimate the proposed system. In particular, unlike the current automatic identification systems, which focus on a limited number of patterns, the proposed method is capable of classifying mixed patterns of proteins in microscope images and can handle the subcellular multi-label protein classification task including 28 subcellular localization patterns. The proposed framework based on deep convolutional neural network outperformed the existing approaches with a F1-score of 0.823, which illustrates the robustness and effectiveness of the proposed system. CONCLUSION: This study proposed a high-performance recognition system for protein atlas classification based on deep learning, and it achieved an automatic multi-label human protein atlas identification framework with superior performance than previous studies.

Efficient skin lesion segmentation using separable-Unet with stochastic weight averaging.

BACKGROUND AND OBJECTIVE: Efficient segmentation of skin lesion in dermoscopy images can improve the classification accuracy of skin diseases, which provides a powerful approach for the dermatologists in examining pigmented skin lesions. However, the segmentation is challenging due to the low contrast of skin lesions from a captured image, fuzzy and indistinct lesion boundaries, huge variety of interclass variation of melanomas, the existence of artifacts, etc. In this work, an efficient and accurate melanoma region segmentation method is proposed for computer-aided diagnostic systems. METHOD: A skin lesion segmentation (SLS) method based on the separable-Unet with stochastic weight averaging is proposed in this work. Specifically, the proposed Separable-Unet framework takes advantage of the separable convolutional block and U-Net architectures, which can extremely capture the context feature channel correlation and higher semantic feature information to enhance the pixel-level discriminative representation capability of fully convolutional networks (FCN). Further, considering that the over-fitting is a local optimum (or sub-optimum) problem, a scheme based on stochastic weight averaging is introduced, which can obtain much broader optimum and better generalization. RESULTS: The proposed method is evaluated in three publicly available datasets. The experimental results showed that the proposed approach segmented the skin lesions with an average Dice coefficient of 93.03% and Jaccard index of 89.25% for the International Skin Imaging Collaboration (ISIC) 2016 Skin Lesion Challenge (SLC) dataset, 86.93% and 79.26% for the ISIC 2017 SLC, and 94.13% and 89.40% for the PH2 dataset, respectively. The proposed approach is compared with other state-of-the-art methods, and the results demonstrate that the proposed approach outperforms them for SLS on both melanoma and non-melanoma cases. Segmentation of a potential lesion with the proposed approach in a dermoscopy image requires less than 0.05 s of processing time, which is roughly 30 times faster than the second best method (regarding the value of Jaccard index) for the ISIC 2017 dataset with the same hardware configuration. CONCLUSIONS: We concluded that using the separable convolutional block and U-Net architectures with stochastic weight averaging strategy could enable to obtain better pixel-level discriminative representation capability. Moreover, the considerably decreased computation time suggests that the proposed approach has potential for practical computer-aided diagnose systems, besides provides a segmentation for the specific analysis with improved segmentation performance.

Weakly Supervised Biomedical Image Segmentation by Reiterative Learning.

Recent advances in deep learning have produced encouraging results for biomedical image segmentation; however, outcomes rely heavily on comprehensive annotation. In this paper, we propose a neural network architecture and a new algorithm, known as overlapped region forecast, for the automatic segmentation of gastric cancer images. To the best of our knowledge, this report for the first time describes that deep learning has been applied to the segmentation of gastric cancer images. Moreover, a reiterative learning framework that achieves superior performance without pretraining or further manual annotation is presented to train a simple network on weakly annotated biomedical images. We customize the loss function to make the model converge faster while avoiding becoming trapped in local minima. Patch boundary errors were eliminated by our overlapped region forecast algorithm. By studying the characteristics of the model trained using two different patch extraction methods, we train iteratively and integrate predictions and weak annotations to improve the quality of the training data. Using these methods, a mean Intersection over Union coefficient of 0.883 and a mean accuracy of 91.09% were achieved on the partially labeled dataset, thereby securing a win in the 2017 China Big Data and Artificial Intelligence Innovation and Entrepreneurship Competition.

Angle aided circle detection based on randomized Hough transform and its application in welding spots detection.

The Hough transform has been widely used in image analysis and digital image processing due to its capability of transforming image space detection to parameter space accumulation. In this paper, we propose a novel Angle-Aided Circle Detection (AACD) algorithm based on the randomized Hough transform to reduce the computational complexity of the traditional Randomized Hough transform. The algorithm ameliorates the sampling method of random sampling points to reduce the invalid accumulation by using region proposals method, and thus significantly reduces the amount of computation. Compared with the traditional Hough transform, the proposed algorithm is robust and suitable for multiple circles detection under complex conditions with strong anti-interference capacity. Moreover, the algorithm has been successfully applied to the welding spot detection on automobile body, and the experimental results verifies the validity and accuracy of the algorithm.

Modular design and development methodology for robotic multi-axis F/M sensors.

Accurate Force/Moment (F/M) measurements are required in many applications, and multi-axis F/M sensors have been utilized a wide variety of robotic systems since 1970s. A multi-axis F/M sensor is capable of measuring multiple components of force terms along x-, y-, z-axis (Fx, Fy, Fz), and the moments terms about x-, y- and z-axis (Mx, My and Mz) simultaneously. In this manuscript, we describe experimental and theoretical approaches for using modular Elastic Elements (EE) to efficiently achieve multi-axis, high-performance F/M sensors. Specifically, the proposed approach employs combinations of simple modular elements (e.g. lamella and diaphragm) in monolithic constructions to develop various multi-axis F/M sensors. Models of multi-axis F/M sensors are established, and the experimental results indicate that the new approach could be widely used for development of multi-axis F/M sensors for many other different applications.