Cognitive Robotics最新文献

In view of the current knowledge graph embedding, it mainly focuses on symmetry/opposition, inversion and combination of relationship patterns, and does not fully consider the structure of the knowledge graph. We propose a Knowledge Graph Embedding Based on Semantic Hierarchy (SHKE), which fully considers the information of knowledge graph by fusing the semantic information of the knowledge graph and the hierarchical information. The knowledge graph is mapped to a polar coordinate system, where concentric circles naturally reflect the hierarchy, and entities can be divided into modulus parts and phase parts, and then the modulus part of the polar coordinate system is mapped to the relational vector space through the relational vector, thus the modulus part takes into account the semantic information of the knowledge graph, and the phase part takes into account the hierarchical information. Experiments show that compared with other models, the proposed model improves the knowledge graph link prediction index Hits@10% by about 10% and the accuracy of the triple group classification experiment by about 10%.

Traditional digital image processing methods extract disease features manually, which have low efficiency and low recognition accuracy. To solve this problem, In this paper, we propose a convolutional neural network architecture FL-EfficientNet (Focal loss EfficientNet), which is used for multi-category identification of plant disease images. Firstly, through the Neural Architecture Search technology, the network width, network depth, and image resolution are adaptively adjusted according to a group of composite coefficients, to improve the balance of network dimension and model stability; Secondly, the valuable features in the disease image are extracted by introducing the moving flip bottleneck convolution and attention mechanism; Finally, the Focal loss function is used to replace the traditional Cross-Entropy loss function, to improve the ability of the network model to focus on the samples that are not easy to identify. The experiment uses the public data set new plant diseases dataset (NPDD) and compares it with ResNet50, DenseNet169, and EfficientNet. The experimental results show that the accuracy of FL-EfficientNet in identifying 10 diseases of 5 kinds of crops is 99.72%, which is better than the above comparison network. At the same time, FL-EfficientNet has the fastest convergence speed, and the training time of 15 epochs is 4.7 h.

In previous research on dementia discrimination by voice, a method using multiple acoustic features by machine learning has been proposed. However, they do not focus on speech analysis in mild dementia patients (MCI). Therefore, we propose a dementia discrimination system based on the analysis of vowel utterance features. The analysis results indicated that some cases of dementia appeared in the voice of mild dementia patients. These results can also be used as an index for future improvement of speech sounds in dementia. Taking advantage of these results, we propose an ensemble discrimination system using a classifier with statistical acoustic features and a Neural Network of transformer models, and the F-score is 0.907, which is better than the state-of-the-art methods.

Joint extracting entities and relations from unstructured text is a fundamental task in information extraction and a key step in constructing large knowledge graphs, entities and relations are constructed as relational triples of the form (subject, relation, object) or (s, r, o). Although triple extraction has been extremely successful, there are still continuing challenges due to factors such as entity overlap. Recent work has shown us the excellent performance of joint extraction models, however these methods still suffer from some problems, such as the redundancy prediction problem. Traditional methods for solving the overlap problem require triple extraction under the full class of relations defined in the dataset, however the number of relations in a sentence is much smaller than the full relational class, which leads to a large number of redundant predictions. To solve this problem, this paper decomposes the task into two steps: entity and potential relation extraction and entity-semantic role determination of triples. Specifically, we design several modules to extract the entities and relations in the sentence separately, and we use these entities and relations to construct possible candidate triples and predict the semantic roles (subject or object) of the entities under the relational constraints to obtain the correct triples. In general we propose a model for identifying the semantic roles of entities in triples under relation constraints, which can effectively solve the problem of redundant prediction, We also evaluated our model on two widely used public datasets, and our model achieved advanced performance with F1 scores of 90.8 and 92.4 on NYT and WebNLG, respectively.

With the advent of the information society, the eyes' health is threatened all over the world. Rules and systems have been proposed to avoid these problems, but most users do not use them due to the physical and time constraints and costs involved and the lack of awareness of eye health. In this paper, we estimate the eye fatigue sensitivity by detecting spontaneous blinks with high accuracy. The experimental results show that the proposed Eye Aspect Ratio Mapping can classify blinks with high accuracy at a low cost. We also found a strong correlation between the median SBR (Spontaneous Blink Rate) and the time between the objective estimation of eye fatigue and the subject's awareness of eye fatigue.

In recent years, researchers have paid more attention to the hybrid applications of quantum computing and brain-computer interfaces. With the development of neural technology and artificial intelligence, scientists have become more and more researching brain-computer interface, and the application of brain-computer interface technology to more fields has gradually become the focus of research. While the field of brain-computer interface has evolved rapidly over the past decades, the core technologies and innovative ideas behind seemingly unrelated brain-computer interface systems are rarely summarized from the point of integration with quantum. This paper provides a detailed report on the hybrid applications of quantum computing and brain-computer interface, indicates the current problems, and gives suggestions on the hybrid application research direction.

The development of a language of action representation is a central issue for cognitive robotics, motor neuroscience, ergonomics, sport, and arts with a double goal: analysis and synthesis of action sequences that preserve the spatiotemporal invariants of biological motion, including the associated goals of learning and training. However, the notation systems proposed so far only achieved inconclusive results. By reviewing the underlying rationale of such systems, it is argued that the common flaw is the choice of the ‘primitives’ to be combined to produce complex gestures: basic movements with a different degree of “granularity”. The problem is that in motor cybernetics movements do not add: whatever the degree of granularity of the chosen primitives their simple summation is unable to produce the spatiotemporal invariants that characterize biological motion. The proposed alternative is based on the Equilibrium Point Hypothesis and, in particular, on a computational formulation named Passive Motion Paradigm, where whole-body gestures are produced by applying a small set of force fields to specific key points of the internal body schema: its animation by carefully selected force fields is analogous to the animation of a marionette using wires or strings. The crucial point is that force fields do add, thus suggesting to use force fields as a consistent set of primitives instead of basic movements. This is the starting point for suggesting a force field-based language of action representation, named Pinocchio in analogy with the famous marionette. The proposed language for action description and generation includes three main modules: 1) Primitive force field generators, 2) a Body-Model to be animated by the primitive generators, and 3) a graphical staff system for expressing any specific notated gesture. We suggest that such language is a crucial building block for the development of a cognitive architecture of cooperative robots.

The term "collaborative robot" is commonly known as Cobot, which refers to a partnership between a robot and a human. Aside from providing physical contact between a robot and a person on the same production line simultaneously, the Cobot is designed as user-friendly. They enable operators to respond immediately to work done by the robot based on the company's urgent needs. This paper aims to explore the potential of Cobots in manufacturing. Cobots are widely employed in various industries such as life science, automotive, manufacturing, electronics, aerospace, packaging, plastics, and healthcare. For many of these businesses, the capacity to maintain a lucrative man-machine shared workplace can provide a considerable competitive edge. Cobots are simple to use while being dependable, safe, and precise. A literature review was carried out from the database from ScienceDirect, Scopus, Google Scholar, ResearchGate and other research platforms on the keyword “Cobots” or “Collaborative robots” for manufacturing. The Paper briefly discusses and provides the capabilities of this technology in manufacturing. Cobots are programmed to do crucial things such as handling poisonous substances, from putting screws on a vehicle body to cooking a meal, etc. Human operators can readily control this technology remotely and perform dangerous jobs. This paper's overview of Cobots and how it is differentiated from Robot is briefly described. The typical Features, Capabilities, Collaboration & Industrial Scenarios with Cobots are also discussed briefly. Further, the study identified and discussed the significant applications of Cobots for manufacturing. Cobots are utilised in several methods and a wide range of application areas. These elevate manufacturing and other operations to new heights. They also collaborate with humans to balance the demand for safety and the need for flexibility and efficiency.

Named entity recognition (NER) is a fundamental and important task in natural language processing. Existing methods attempt to utilize convolutional neural network (CNN) to solve NER task. However, a disadvantage of CNN is that it fails to obtain the global information of texts, leading to an unsatisfied performance on medical NER task. In view of the disadvantages of CNN in medical NER task, this paper proposes to utilize the dilated convolutional neural network (DCNN) and bidirectional long short-term memory (BiLSTM) for hierarchical encoding, and make use of the advantages of DCNN to capture global information with fast computing speed. At the same time, multiple feature words are inserted into the medical text datasets for improving the performance of medical NER. Extensive experiments are done on three real-world datasets, which demonstrate that our method is superior to the compared models.

With the wide application of robots in life and production, robotic grasping is also experiencing continuous development. However, in practical application, some external environmental factors and the factors of the object itself have an impact on the accuracy of grasping detection. There are many classification methods of grasping detection. In this paper, the parallel gripper is used as the end of grasping to carry out research. Aiming at the angle problem of robot grasping, this paper summarizes some research status of grasping detection from 2D image to 3D space. According to their respective application, advantages, and disadvantages, this paper analyzes the development trend of the two methods. At the same time, several commonly used grasping datasets are introduced and compared.