A New Semantic-Based Tool Detection Method for Robots

Home helper robots have become more acceptable due to their excellent image recognition ability. However, some common household tools remain challenging to recognize, classify, and use by robots. We designed a detection method for the functional components of common household tools based on the mask regional convolutional neural network (Mask-R-CNN). This method is a multitask branching target detection algorithm that includes tool classification, target box regression, and semantic segmentation. It provides accurate recognition of the functional components of tools. The method is compared with existing algorithms on the dataset UMD Part Affordance dataset and exhibits effective instance segmentation and key point detection, with higher accuracy and robustness than two traditional algorithms. The proposed method helps the robot understand and use household tools better than traditional object detection algorithms

An Improved Genetic-Shuffled Frog-Leaping Algorithm for Permutation Flowshop Scheduling

Due to the NP-hard nature, the permutation flowshop scheduling problem (PFSSP) is a fundamental issue for Industry 4.0, especially under higher productivity, efficiency, and self-managing systems. This paper proposes an improved genetic-shuffled frog-leaping algorithm (IGSFLA) to solve the permutation flowshop scheduling problem. In the proposed IGSFLA, the optimal initial frog (individual) in the initialized group is generated according to the heuristic optimal-insert method with fitness constrain. The crossover mechanism is applied to both the subgroup and the global group to avoid the local optimal solutions and accelerate the evolution. To evolve the frogs with the same optimal fitness more outstanding, the disturbance mechanism is applied to obtain the optimal frog of the whole group at the initialization step and the optimal frog of the subgroup at the searching step. The mathematical model of PFSSP is established with the minimum production cycle (makespan) as the objective function, the fitness of frog is given, and the IGSFLA-based PFSSP is proposed. Experimental results have been given and analyzed, showing that IGSFLA not only provides the optimal scheduling performance but also converges effectively

Two-Stream Retentive Long Short-Term Memory Network for Dense Action Anticipation

Analyzing and understanding human actions in long-range videos has promising applications, such as video surveillance, automatic driving, and efficient human-computer interaction. Most researches focus on short-range videos that predict a single action in an ongoing video or forecast an action several seconds earlier before it occurs. In this work, a novel method is proposed to forecast a series of actions and their durations after observing a partial video. This method extracts features from both frame sequences and label sequences. A retentive memory module is introduced to richly extract features at salient time steps and pivotal channels. Extensive experiments are conducted on the Breakfast data set and 50 Salads data set. Compared to the state-of-the-art methods, the method achieves comparable performance in most cases

A RUL Prediction Method of Small Sample Equipment Based on DCNN-BiLSTM and Domain Adaptation

To solve the problem of low accuracy of remaining useful life (RUL) prediction caused by insufficient sample data of equipment under complex operating conditions, an RUL prediction method of small sample equipment based on a deep convolutional neural network—bidirectional long short-term memory network (DCNN-BiLSTM) and domain adaptation is proposed. Firstly, in order to extract the common features of the equipment under the condition of sufficient samples, a network model that combines the deep convolutional neural network (DCNN) and the bidirectional long short-term memory network (BiLSTM) was used to train the source domain and target domain data simultaneously. The Maximum Mean Discrepancy (MMD) was used to constrain the distribution difference and achieve adaptive matching and feature alignment between the target domain samples and the source domain samples. After obtaining the pre-trained model, fine-tuning was used to transfer the network structure and parameters of the pre-trained model to the target domain for training, perform network optimization and finally obtain an RUL prediction model that was more suitable for the target domain data. The method was validated on a simulation dataset of commercial modular aero-propulsion provided by NASA, and the experimental results show that the method improves the prediction accuracy and generalization ability of equipment RUL under cross-working conditions and small sample conditions

A new mixing technique for solidifier and dredged fill in coastal area

One of the major drawbacks of the conventional method of land reclamation, which involves mixing cement with the dredged soils at the disposal site, is the high cost associated with the manufacturing and transportation. In this study, a new solidified dredged fill (SDF) technique and a new additive is proposed and their applications into practice are discussed. Unlike the conventional approach, the dredged marine soils were mixed with the solidifiers using a new designed mixing technique prior to its transport to site, which could significantly reduce the cost of site machinery and effectively reclaim land with adequate engineering properties necessary for the construction of infrastructure. To evaluate the performance of the reclaimed land using the proposed technique, a series of laboratory and field tests (viz. static and dynamic cone penetration tests, plate load tests) were conducted on the ground filled with and without solidified dredged marine soils, respectively. The results show that the engineering behaviour of the reclaimed land with dredged marine soils using SDF technique can be significantly improved. The SDF technique combined with the newly designed mixing system improved the performance of ground and is thus proved to be both cost-effective and safe

A More Effective Zero-DCE Variant: Zero-DCE Tiny

The purpose of Low Illumination Image Enhancement (LLIE) is to improve the perception or interpretability of images taken in low illumination environments. This work inherits the work of Zero-Reference Deep Curve Estimation (Zero-DCE) and proposes a more effective image enhancement model, Zero-DCE Tiny. First, the new model introduces the Cross Stage Partial Network (CSPNet) into the original U-net structure, divides basic feature maps into two parts, and then recombines it through the structure of cross-phase connection to achieve a richer gradient combination with less computation. Second, we replace all the deep separable convolutions except the last layer with Ghost modules, which makes the network lighter. Finally, we introduce the channel consistency loss into the non-reference loss, which further strengthens the constraint on the pixel distribution of the enhanced image and the original image. Experiments show that compared with Zero-DCE++, the network proposed in this work is more lightweight and surpasses the Zero-DCE++ method in some important image enhancement evaluation indexes

GACSNet: A Lightweight Network for the Noninvasive Blood Glucose Detection

Diabetes is a disease that requires long-term monitoring, and noninvasive glucose detection effectively reduces patient self-monitor resistance. Traditional noninvasive blood glucose methods are limited by many aspects, such as equipment, environment, and safety, which are not suitable for practical use. Aiming at this problem, propose a lightweight network called Group Asymmetric Convolution Shuffle Network (GACSNet) for noninvasive blood glucose detection: use infrared imaging to acquire human metabolic heat and construct a dataset, combine asymmetric convolution with channel shuffle unit, the novel convolution neural network is designed, and extract metabolic heat and cool-heat deviation features in thermal imaging. The test set was analyzed and compared using Clarke’s error grid. The current neural network showed an mean absolute percentage error of 9.17%, with a training time of 2 min 54 s and a inference time of 1.35 ms, which was superior to several traditional convolution neural networks’ accuracy, training cost, and real-time performance in the blood glucose region 3.9–9 mmol/L, and provided new insights into noninvasive blood glucose detection

Joint Pedestrian and Body Part Detection via Semantic Relationship Learning

While remarkable progress has been made to pedestrian detection in recent years, robust pedestrian detection in the wild e.g., under surveillance scenarios with occlusions, remains a challenging problem. In this paper, we present a novel approach for joint pedestrian and body part detection via semantic relationship learning under unconstrained scenarios. Specifically, we propose a Body Part Indexed Feature (BPIF) representation to encode the semantic relationship between individual body parts (i.e., head, head-shoulder, upper body, and whole body) and highlight per body part features, providing robustness against partial occlusions to the whole body. We also propose an Adaptive Joint Non-Maximum Suppression (AJ-NMS) to replace the original NMS algorithm widely used in object detection, leading to higher precision and recall for detecting overlapped pedestrians. Experimental results on the public-domain CUHK-SYSU Person Search Dataset show that the proposed approach outperforms the state-of-the-art methods for joint pedestrian and body part detection in the wild

A More Effective Zero-DCE Variant: Zero-DCE Tiny

The purpose of Low Illumination Image Enhancement (LLIE) is to improve the perception or interpretability of images taken in low illumination environments. This work inherits the work of Zero-Reference Deep Curve Estimation (Zero-DCE) and proposes a more effective image enhancement model, Zero-DCE Tiny. First, the new model introduces the Cross Stage Partial Network (CSPNet) into the original U-net structure, divides basic feature maps into two parts, and then recombines it through the structure of cross-phase connection to achieve a richer gradient combination with less computation. Second, we replace all the deep separable convolutions except the last layer with Ghost modules, which makes the network lighter. Finally, we introduce the channel consistency loss into the non-reference loss, which further strengthens the constraint on the pixel distribution of the enhanced image and the original image. Experiments show that compared with Zero-DCE++, the network proposed in this work is more lightweight and surpasses the Zero-DCE++ method in some important image enhancement evaluation indexes