In the industrial field, the anthropomorphism of grasping robots is the trend of future development, however, the basic vision technology adopted by the grasping robot at this stage has problems such ...as inaccurate positioning and low recognition efficiency. Based on this practical problem, in order to achieve more accurate positioning and recognition of objects, an object detection method for grasping robot based on improved YOLOv5 was proposed in this paper. Firstly, the robot object detection platform was designed, and the wooden block image data set is being proposed. Secondly, the Eye-In-Hand calibration method was used to obtain the relative three-dimensional pose of the object. Then the network pruning method was used to optimize the YOLOv5 model from the two dimensions of network depth and network width. Finally, the hyper parameter optimization was carried out. The simulation results show that the improved YOLOv5 network proposed in this paper has better object detection performance. The specific performance is that the recognition precision, recall, mAP value and F1 score are 99.35%, 99.38%, 99.43% and 99.41% respectively. Compared with the original YOLOv5s, YOLOv5m and YOLOv5l models, the mAP of the YOLOv5_ours model has increased by 1.12%, 1.2% and 1.27%, respectively, and the scale of the model has been reduced by 10.71%, 70.93% and 86.84%, respectively. The object detection experiment has verified the feasibility of the method proposed in this paper.
With the improvement of human living standards, users’ requirements have changed from function to emotion. Helping users pick out the most suitable product based on their subjective requirements is ...of great importance for enterprises. This paper proposes a Kansei engineering-based grey relational analysis and techniques for order preference by similarity to ideal solution (KE-GAR-TOPSIS) method to make a subjective user personalized ranking of alternative products. The KE-GRA-TOPSIS method integrates five methods, including Kansei Engineering (KE), analytic hierarchy process (AHP), entropy, game theory, and grey relational analysis-TOPSIS (GRA-TOPSIS). First, an evaluation system is established by KE and AHP. Second, we define a matrix variate—Kansei decision matrix (KDM)—to describe the satisfaction of user requirements. Third, the AHP is used to obtain subjective weight. Next, the entropy method is employed to obtain objective weights by taking the KDM as input. Then the two types of weights are optimized using game theory to obtain the comprehensive weights. Finally, the GRA-TOPSIS method takes the comprehensive weights and the KMD as inputs to rank alternatives. A comparison of the KE-GRA-TOPSIS, KE-TOPSIS, KE-GRA, GRA-TOPSIS, and TOPSIS is conducted to illustrate the unique merits of the KE-GRA-TOPSIS method in Kansei evaluation. Finally, taking the electric drill as an example, we describe the process of the proposed method in detail, which achieves a symmetry between the objectivity of products and subjectivity of users.
Metal–organic frameworks (MOFs) are promising high surface area coordination polymers with tunable pore structures and functionality; however, a lack of good size and morphological control over the ...as‐prepared MOFs has persisted as an issue in their application. Herein, we show how a robust protein template, tobacco mosaic virus (TMV), can be used to regulate the size and shape of as‐fabricated MOF materials. We were able to obtain discrete rod‐shaped TMV@MOF core–shell hybrids with good uniformity, and their diameters could be tuned by adjusting the synthetic conditions, which can also significantly impact the stability of the core–shell composite. More interestingly, the virus particle underneath the MOF shell can be chemically modified using a standard bioconjugation reaction, showing mass transportation within the MOF shell.
It's a wrap: Sleeve netting provides a cost‐effective solution to keep fresh fruits safe and sound. A metal–organic framework (MOF) is used to construct a molecular protective netting on the surface of the rod‐like tobacco mosaic virus. The shell thickness was discovered to play a crucial role in the stability of the core–shell composite. More interestingly, the embedded virus particle can be chemically modified using a standard bioconjugation reaction, showing mass transportation within the MOF shell.
In this paper, we propose a broad learning system based on the sparrow search algorithm. Firstly, in order to avoid the complicated manual parameter tuning process and obtain the best combination of ...hyperparameters, the sparrow search algorithm is used to optimize the shrinkage coefficient (r) and regularization coefficient (λ) in the broad learning system to improve the prediction accuracy of the model. Second, using the broad learning system to build a network interface flow forecasting model. The flow values in the time period T-11,T are used as the characteristic values of the traffic at the moment T+1. The hyperparameters outputted in the previous step are fed into the network to train the broad learning system network traffic prediction model. Finally, to verify the model performance, this paper trains the prediction model on two public network flow datasets and real traffic data of an enterprise cloud platform switch interface and compares the proposed model with the broad learning system, long short-term memory, and other methods. The experiments show that the prediction accuracy of this method is higher than other methods, and the moving average reaches 97%, 98%, and 99% on each dataset, respectively.
With the rapid development of machine learning, its powerful function in the machine vision field is increasingly reflected. The combination of machine vision and robotics to achieve the same precise ...and fast grasping as that of humans requires high-precision target detection and recognition, location and reasonable grasp strategy generation, which is the ultimate goal of global researchers and one of the prerequisites for the large-scale application of robots. Traditional machine learning has a long history and good achievements in the field of image processing and robot control. The CNN (convolutional neural network) algorithm realizes training of large-scale image datasets, solves the disadvantages of traditional machine learning in large datasets, and greatly improves accuracy, thereby positioning CNNs as a global research hotspot. However, the increasing difficulty of labeled data acquisition limits their development. Therefore, unsupervised learning, self-supervised learning and reinforcement learning, which are less dependent on labeled data, have also undergone rapid development and achieved good performance in the fields of image processing and robot capture. According to the inherent defects of vision, this paper summarizes the research achievements of tactile feedback in the fields of target recognition and robot grasping and finds that the combination of vision and tactile feedback can improve the success rate and robustness of robot grasping. This paper provides a systematic summary and analysis of the research status of machine vision and tactile feedback in the field of robot grasping and establishes a reasonable reference for future research.
Biomimetic mineralization with metal–organic frameworks (MOF), typically zeolitic imidazolate framework-8 (ZIF-8), is an emerging strategy to protect sensitive biological substances against ...denaturing environmental stressors such as heat and proteolytic agents. Additionally, this same biomimetic mineralization process has the potential of being used to create distinct core–shell architectures using genetically or chemically modified viral nanoparticles. Despite the proliferation of examples for ZIF-8 growth on biological or proteinaceous substrates, systematic studies of these processes are few and far between. Herein, we employed the tobacco mosaic virus (TMV) as a model biological template to investigate the biomimetic mineralization of ZIF-8, which has been proven to be a robust MOF for encasing and protecting inlaid biological substances. Our study shows a systematic dependence upon ZIF-8 crystallization parameters, e.g., ligand to metal molar ratio and metal concentration, which can yield several distinct morphologies of TMV@ZIF-8 composites and phases of ZIF-8. Further investigation using charged synthetic conjugates, time dependent growth analysis, and calorimetric analysis has shown that the TMV–Zn interaction plays a pivotal role in the final morphology of the TMV@ZIF-8, which can take the form of either core–shell bionanoparticles or large crystals of ZIF-8 with entrapped TMV located exclusively on the outer facets. The design rules outlined here, it is hoped, will provide guidance in biomimetic mineralization of MOFs on proteinaceous materials using ZIF-8.
A sub-bottom profiler (SBP) can capture the sediment interfaces and properties of different types of sediment. Horizon picking from SBP images is one of the most crucial steps in marine sub-bottom ...sediment interpretation. However, traditional horizon picking methods are good at obtaining the main horizons representing the main reflectors while ignoring the detailed horizons. While detailed horizons are the prime objective, many tiny structures caused by interference echoes will also be picked. To overcome this limitation, an integrated horizon picking method for obtaining the main and detailed horizons simultaneously is proposed in this paper. A total of three main process steps: the diffusion filtering method, the enhancement filtering method as well as the local phase calculation method, are used to help obtain the main and detailed horizons. The diffusion filtering method smooths the SBP images and preserves reflectors. Enhancement filtering can eliminate outliers and enhance reflectors. The local phase can be used to highlight all of the reflections and help in the choosing of detailed horizons. A series of experiments were then performed to validate the effectiveness of the proposed method, and good performances were achieved.
Many text mining tasks such as text retrieval, text summarization, and text comparisons depend on the extraction of representative keywords from the main text. Most existing keyword extraction ...algorithms are based on discrete bag-of-words type of word representation of the text. In this paper, we propose a patent keyword extraction algorithm (PKEA) based on the distributed Skip-gram model for patent classification. We also develop a set of quantitative performance measures for keyword extraction evaluation based on information gain and cross-validation, based on Support Vector Machine (SVM) classification, which are valuable when human-annotated keywords are not available. We used a standard benchmark dataset and a homemade patent dataset to evaluate the performance of PKEA. Our patent dataset includes 2500 patents from five distinct technological fields related to autonomous cars (GPS systems, lidar systems, object recognition systems, radar systems, and vehicle control systems). We compared our method with Frequency, Term Frequency-Inverse Document Frequency (TF-IDF), TextRank and Rapid Automatic Keyword Extraction (RAKE). The experimental results show that our proposed algorithm provides a promising way to extract keywords from patent texts for patent classification.
The condition of a product (i.e., being used or new) plays an important role in consumer judgment and purchase decisions, yet this phenomenon has been relatively under-researched. Using an ...experimental approach, this study investigates the effect of embarrassment on choice of used and new products. We demonstrate that due to the embarrassment associated with buying used products, Chinese consumers are more likely than their US counterparts to choose new products over used ones. Furthermore, we find that this difference can be attenuated by reframing used products positively as antiques. Our findings enrich the theoretical understanding of consumer behavior across cultures, offer important managerial implications, and provide novel insights into future research directions.
Convolutional neural networks (CNNs) with log-mel audio representation and CNN-based end-to-end learning have both been used for environmental event sound recognition (ESC). However, log-mel features ...can be complemented by features learned from the raw audio waveform with an effective fusion method. In this paper, we first propose a novel stacked CNN model with multiple convolutional layers of decreasing filter sizes to improve the performance of CNN models with either log-mel feature input or raw waveform input. These two models are then combined using the Dempster–Shafer (DS) evidence theory to build the ensemble DS-CNN model for ESC. Our experiments over three public datasets showed that our method could achieve much higher performance in environmental sound recognition than other CNN models with the same types of input features. This is achieved by exploiting the complementarity of the model based on log-mel feature input and the model based on learning features directly from raw waveforms.
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