Biped robots have received increasing attention due to their human-like mechanical structure and good environmental adaptability. In this paper, a new Human-Simulated Intelligent Walking Control (HIWC) scheme is proposed to solve the stability problem of the most popular proportional differential (PD) control under model inaccuracy and disturbance, and further improve its control performance. Specifically, based on Human-Simulated Intelligent Control (HSIC), HIWC is a hierarchical control structure composed of a foot placement compensation (FPC) strategy at the high-level planning layer, and a multi-mode compensation controller (MCC) at the low-level (execution) layer. In FPC, a foot placement compensation algorithm is proposed to plan and correct the swing foots trajectory in real time. MCC consists of a PD and two adaptive compensation algorithms. MCC under bounded uncertainty is proven to be stable in this paper using the Lyapunov theorem. HIWC was tested and compared with PD and model predictive control (MPC) in three experiments on a physical robot platform for planar walking, push-pull, and uneven-ground walking. Experimental results show that the proposed HIWC is more flexible and accurate in controlling the robot’s movement.Note to Practitioners—This paper builds on the fact that PD controllers cannot be easily proven to be stable and do not provide accurate control for the biped robot walking problem. To address these issues, this paper proposes a novel control scheme namely Human-Simulated Intelligent Walking Control (HIWC) and belonging to the family of Human-Simulated Intelligent Control (HSIC) schemes. The proposed HIWC system has been compared against the model predictive control (MPC) and a proportional differential (PD) controller. The proposed HIWC, unlike PD controllers, is rigorously proven to be stable in the presence of model inaccuracy and disturbance. Furthermore, the experiments carried out on a real-world biped robot demonstrate the superiority of HIWC over PD and MPC in terms of control performance.

The optimization of robot controller parameters is a crucial task for enhancing robot performance, yet it often presents challenges due to the complexity of multi-objective, multi-dimensional multi-parameter optimization. This paper introduces a novel approach aimed at efficiently optimizing robot controller parameters to enhance its motion performance. While spiking neural P systems have shown great potential in addressing optimization problems, there has been limited research and validation concerning their application in continuous numerical, multi-objective, and multi-dimensional multi-parameter contexts. To address this research gap, our paper proposes the Entropy-Weighted Numerical Gradient Optimization Spiking Neural P System, which combines the strengths of entropy weighting and spiking neural P systems. First, the introduction of entropy weighting eliminates the subjectivity of weight selection, enhancing the objectivity and reproducibility of the optimization process. Second, our approach employs parallel gradient descent to achieve efficient multi-dimensional multi-parameter optimization searches. In conclusion, validation results on a biped robot simulation model show that our method markedly enhances walking performance compared to traditional approaches and other optimization algorithms. We achieved a velocity mean absolute error at least 35% lower than other methods, with a displacement error two orders of magnitude smaller. This research provides an effective new avenue for performance optimization in the field of robotics.

This paper presents a special form of the general Intelligent Portfolio Theory for arbitrage in commodity futures markets. The theory is based on the recognition of the reality that any single arbitrage strategy on any futures pair is bounded in rationality, so it is unable to keep exhibiting desirable performance consistently over time; thus an intelligent portfolio for arbitrage in futures markets consists of a number of selected futures pairs of which each pair is arbitraged via one or multiple dynamic strategies. Here integration test and correlation are used for selecting futures pairs, and Kalman filtering is used to model the trend of the price spread for each futures pair. In Chinese commodity futures markets, 32 futures are examined, from which up to 14 futures pairs are selected. This intelligent portfolio arbitrage system is tested on historical intraday data, and the result exhibits desirable performance with low maximum drawdown and high reward-to-risk ratio in comparison with individual futures pair arbitrages.

Audio streaming services have become increasingly popular due to the wide use of smart phones. More and more people are enjoying live audio broadcasting while they are doing various kinds of activities. Meanwhile, the data volume of live audio streams is also ever increasing. Searching and indexing these audio streams is still an important and open problem, with the following challenges: (i) queries on the large number of audio streams need to be answered in real-time; (ii) a live audio stream is inserted into the index continuously to enable live audio streams to appear in query results, and the number of insertions is large which often becomes a performance issue. In this application paper, we propose a multi-modal and unified log structured merge-tree (a.k.a. LSM-tree which consists of multiple inverted indices) based index to support intensive insertions and real-time search on live audio stream applications. Our index natively supports two major types of indexing techniques in audio search: text based indexing and sound based indexing. To address the challenges of live audio indexing and search, we propose an index (called RTSI) which avoids traversing multiple inverted indices to compute the score of an audio stream. In RTSI, we propose various techniques to address the technical challenges. First, for each term we use three inverted lists which contain the sorted score of popularity, freshness and relevance, respectively, such that we can compute the top-k query results efficiently. Second, we devise an upper bound for the unchecked audio streams, such that the query answering process can be terminated earlier. Third, we create mirrors for the indices that need to be merged, such that queries can be answered in real-time when the indices are merging. We conduct extensive experiments on audio streams obtained from Ximalaya. The experimental results show that RTSI can answer a large number of queries in a real-time manner while concurrently handling massive insertions.

As the key point of the ontology research field, concept similarity computation has been widely used in the automatic question-answering systems. This paper firstly analyzes the existing concept similarity computation algorithm which is based on the semantic distance, and then proposes a new computation algorithm formula by modifying and adding some factors which can affect the accuracy of concept similarity computation. Finally, the experimental results show that the proposed algorithm offers better effectiveness for the concept similarity computation.