In this paper, we aim to illustrate the concept of mutually trustworthy human-machine knowledge automation (HM-KA) as the technical mechanism of hybrid augmented intelligence (HAI) based complex system cognition, management, and control (CMC). We describe the historical development of complex system science and analyze the limitations of human intelligence and machine intelligence. The need for using human-machine HAI in complex systems is then explained in detail. The concept of “mutually trustworthy HM-KA” mechanism is proposed to tackle the CMC challenge, and its technical procedure and pathway are demonstrated using an example of corrective control in bulk power grid dispatch. It is expected that the proposed mutually trustworthy HM-KA concept can provide a novel and canonical mechanism and benefit real-world practices of complex system CMC.
Affective brain–computer interfaces have become an increasingly important topic to achieve emotional intelligence in human–machine collaboration. However, due to the complexity of electroencephalogram (EEG) signals and the individual differences in emotional response, it is still a great challenge to design a reliable and effective model. Considering the influence of personality traits on emotional response, it would be helpful to integrate personality information and EEG signals for emotion recognition. This study proposes a personality-guided attention neural network that can use personality information to learn effective EEG representations for emotion recognition. Specifically, we first use a convolutional neural network to extract rich temporal and regional representations of EEG signals, and a special convolution kernel is designed to learn inter- and intra-regional correlations simultaneously. Second, inspired by the fact that electrodes within distinct brain scalp regions play different roles in emotion recognition, a personality-guided regional-attention mechanism is proposed to further explore the contributions of electrodes within a region and between regions. Finally, attention-based long short-term memory is designed to explore the temporal dynamics of EEG signals. Experiments on the AMIGOS dataset, which is a dataset for multimodal research for affect, personality traits, and mood on individuals and groups, show that the proposed method can significantly improve the performance of subject-independent emotion recognition and outperform state-of-the-art methods.
In this study, a novel reinforcement learning task supervisor (RLTS) with memory in a behavioral control framework is proposed for human–multi-robot coordination systems (HMRCSs). Existing HMRCSs suffer from high decision-making time cost and large task tracking errors caused by repeated human intervention, which restricts the autonomy of multi-robot systems (MRSs). Moreover, existing task supervisors in the null-space-based behavioral control (NSBC) framework need to formulate many priority-switching rules manually, which makes it difficult to realize an optimal behavioral priority adjustment strategy in the case of multiple robots and multiple tasks. The proposed RLTS with memory provides a detailed integration of the deep Q-network (DQN) and long short-term memory (LSTM) knowledge base within the NSBC framework, to achieve an optimal behavioral priority adjustment strategy in the presence of task conflict and to reduce the frequency of human intervention. Specifically, the proposed RLTS with memory begins by memorizing human intervention history when the robot systems are not confident in emergencies, and then reloads the history information when encountering the same situation that has been tackled by humans previously. Simulation results demonstrate the effectiveness of the proposed RLTS. Finally, an experiment using a group of mobile robots subject to external noise and disturbances validates the effectiveness of the proposed RLTS with memory in uncertain real-world environments.
At present, focused crawler is a crucial method for obtaining effective domain knowledge from massive heterogeneous networks. For most current focused crawling technologies, there are some difficulties in obtaining high-quality crawling results. The main difficulties are the establishment of topic benchmark models, the assessment of topic relevance of hyperlinks, and the design of crawling strategies. In this paper, we use domain ontology to build a topic benchmark model for a specific topic, and propose a novel multiple-filtering strategy based on local ontology and global ontology (MFSLG). A comprehensive priority evaluation method (CPEM) based on the web text and link structure is introduced to improve the computation precision of topic relevance for unvisited hyperlinks, and a simulated annealing (SA) method is used to avoid the focused crawler falling into local optima of the search. By incorporating SA into the focused crawler with MFSLG and CPEM for the first time, two novel focused crawler strategies based on ontology and SA (FCOSA), including FCOSA with only global ontology (FCOSA_G) and FCOSA with both local ontology and global ontology (FCOSA_LG), are proposed to obtain topic-relevant webpages about rainstorm disasters from the network. Experimental results show that the proposed crawlers outperform the other focused crawling strategies on different performance metric indices.
Software developers often write code that has similar functionality to existing code segments. A code recommendation tool that helps developers reuse these code fragments can significantly improve their efficiency. Several methods have been proposed in recent years. Some use sequence matching algorithms to find the related recommendations. Most of these methods are time-consuming and can leverage only low-level textual information from code. Others extract features from code and obtain similarity using numerical feature vectors. However, the similarity of feature vectors is often not equivalent to the original code’s similarity. Structural information is lost during the process of transforming abstract syntax trees into vectors. We propose an approximate sub-tree matching based method to solve this problem. Unlike existing tree-based approaches that match feature vectors, it retains the tree structure of the query code in the matching process to find code fragments that best match the current query. It uses a fast approximation sub-tree matching algorithm by transforming the sub-tree matching problem into the match between the tree and the list. In this way, the structural information can be used for code recommendation tasks that have high time requirements. We have constructed several real-world code databases covering different languages and granularities to evaluate the effectiveness of our method. The results show that our method outperforms two compared methods, SENSORY and Aroma, in terms of the recall value on all the datasets, and can be applied to large datasets.
To tackle the problem of aquatic environment pollution, a vision-based autonomous underwater garbage cleaning robot has been developed in our laboratory. We propose a garbage detection method based on a modified YOLOv4, allowing high-speed and high-precision object detection. Specifically, the YOLOv4 algorithm is chosen as a basic neural network framework to perform object detection. With the purpose of further improvement on the detection accuracy, YOLOv4 is transformed into a four-scale detection method. To improve the detection speed, model pruning is applied to the new model. By virtue of the improved detection methods, the robot can collect garbage autonomously. The detection speed is up to 66.67 frames/s with a mean average precision (mAP) of 95.099%, and experimental results demonstrate that both the detection speed and the accuracy of the improved YOLOv4 are excellent.
In this study, we develop an adaptive neural network based boundary control method for a flexible marine riser system with unknown nonlinear disturbances and output constraints to suppress vibrations. We begin with describing the dynamic behavior of the riser system using a distributed parameter system with partial differential equations. To compensate for the effect of nonlinear disturbances, we construct a neural network based boundary controller using a radial basis neural network to reduce vibrations. Under the proposed boundary controller, the state of the riser is guaranteed to be uniformly bounded based on the Lyapunov method. The proposed methodology provides a way to integrate neural networks into boundary control for other flexible robotic manipulator systems. Finally, numerical simulations are given to demonstrate the effectiveness of the proposed control method.
Traditional matrix-based approaches in the field of finite state machines construct state transition matrices, and then use the powers of the state transition matrices to represent corresponding dynamic transition processes, which are cornerstones of system analysis. In this study, we propose a static matrix-based approach that revisits a finite state machine from its structure rather than its dynamic transition process, thus avoiding the “explosion of complexity” problem inherent in the existing approaches. Based on the static approach, we reexamine the issues of closed-loop detection and controllability for deterministic finite state machines. In addition, we propose controllable equivalent form and minimal controllable equivalent form concepts and give corresponding algorithms.
Training a machine learning model with federated edge learning (FEEL) is typically time consuming due to the constrained computation power of edge devices and the limited wireless resources in edge networks. In this study, the training time minimization problem is investigated in a quantized FEEL system, where heterogeneous edge devices send quantized gradients to the edge server via orthogonal channels. In particular, a stochastic quantization scheme is adopted for compression of uploaded gradients, which can reduce the burden of per-round communication but may come at the cost of increasing the number of communication rounds. The training time is modeled by taking into account the communication time, computation time, and the number of communication rounds. Based on the proposed training time model, the intrinsic trade-off between the number of communication rounds and per-round latency is characterized. Specifically, we analyze the convergence behavior of the quantized FEEL in terms of the optimality gap. Furthermore, a joint data-and-model-driven fitting method is proposed to obtain the exact optimality gap, based on which the closed-form expressions for the number of communication rounds and the total training time are obtained. Constrained by the total bandwidth, the training time minimization problem is formulated as a joint quantization level and bandwidth allocation optimization problem. To this end, an algorithm based on alternating optimization is proposed, which alternatively solves the subproblem of quantization optimization through successive convex approximation and the subproblem of bandwidth allocation by bisection search. With different learning tasks and models, the validation of our analysis and the near-optimal performance of the proposed optimization algorithm are demonstrated by the simulation results.
Designing logic circuits using complementary metal-oxide-semiconductor (CMOS) technology at the nano scale has been faced with various challenges recently. Undesirable leakage currents, the short-effect channel, and high energy dissipation are some of the concerns. Quantum-dot cellular automata (QCA) represent an appropriate alternative for possible CMOS replacement in the future because it consumes an insignificant amount of energy compared to the standard CMOS. The key point of designing arithmetic circuits is based on the structure of a 1-bit full adder. A low-complexity full adder block is beneficial for developing various intricate structures. This paper represents scalable 1-bit QCA full adder structures based on cell interaction. Our proposed full adders encompass preference aspects of QCA design, such as a low number of cells used, low latency, and small area occupation. Also, the proposed structures have been expanded to larger circuits, including a 4-bit ripple carry adder (RCA), a 4-bit ripple borrow subtractor (RBS), an add/sub circuit, and a 2-bit array multiplier. All designs were simulated and verified using QCA Designer-E version 2.2. This tool can estimate the energy dissipation as well as evaluate the performance of the circuits. Simulation results showed that the proposed designs are efficient in complexity, area, latency, cost, and energy dissipation.