Publications

This study designs and evaluates the bounce performance of a motor-driven, three-degree-of-freedom (3-DoF) single-leg robot. A power-constrained control strategy is developed and implemented in simulations on an Ubuntu 20.04 platform. To assess system performance, we conduct comparative experiments across 15 different configurations, varying leg lengths (300 mm, 400 mm, and 500 mm) and motor models (A1, B1-16, SETZ-1FE, SETZ-1FC, and SETZ-1FD). The results indicate that the SETZ90-1FE motor delivers the best performance, achieving a maximum bounce height of 1.11 m with a 500 mm leg, compared to 0.78 m with a 300 mm leg and 0.87 m with a 400 mm leg. Based on these findings, we select the SETZ90-1FE motor with a 400 mm leg length to further analyze the influence of different joints on bounce performance. Simulation results reveal that the knee joint has the most significant impact, reaching a maximum bounce height of 1.15 m, while the hip and ankle joints achieve bounce heights of 0.80 m and 0.725 m, respectively.

Today, there is an increasing concern about malicious attacks on various networks in society and industry, against which the network robustness is critical. Network connectivity robustness, in particular, is of fundamental importance, which is generally measured by a sequence of calculated values that indicate the connectedness of the remaining network after a sequence of attacks by means of node- or edge-removal. It is computationally time-consuming, however, to measure and evaluate the network connectivity robustness using the conventional attack simulations, especially for large-scale networked systems. In the present paper, an efficient robustness predictor based on multiple convolutional neural networks (mCNN-RP) is proposed for predicting the network connectivity robustness, which is an natural extension of the single CNN-based predictor. In mCNN-RP, one CNN works as the classifier, while each of the rest CNNs works as an estimator for predicting the connectivity robustness of every classified network category. The network categories are classified according to the available prior knowledge. A data-based filter is installed for predictive data refinement. Extensive experimental studies on both synthetic and real-world networks, including directed and undirected as well as weighted and unweighted topologies, verify the effectiveness of mCNN-RP. The results demonstrate that the average prediction error is lower than the standard deviation of the tested data, which outperforms the single CNN-based framework. The runtime in assessing network connectivity robustness is significantly reduced by using the CNN-based technique. The proposed mCNN-RP not only can accurately predict the connectivity robustness of various complex networks, but also provides an excellent indicator for the connectivity robustness, better than other existing prediction measures.

This study is concerned with the dynamical behaviors of epidemic spreading over a two-layered interconnected network. Three models in different levels are proposed to describe cooperative spreading processes over the interconnected network, wherein the disease in one network can spread to the other. Theoretical analysis is provided for each model to reveal that the global epidemic threshold in the interconnected network is not larger than the epidemic thresholds for the two isolated layered networks. In particular, in an interconnected homogeneous network, a detailed theoretical analysis is presented which allows quick and accurate calculations of the global epidemic threshold. Moreover, in an interconnected heterogeneous network with interlayer correlation between node degrees, it is found that the interlayer correlation coefficient has little impact on the epidemic threshold but has significant impact on the total prevalence. Simulations further verify the analytical results, showing that cooperative epidemic processes promote the spreading of diseases.

In this paper, a class of penalty-function-type multi-agent approaches via communication networks is developed for distributed nonconvex optimal resource allocation. A penalty-function-type method is utilized to handle networked resource allocation constraints, and a multi-agent method is employed for handling global information in a distributed manner. Then, a penalty-function-type multi-agent system is constructed for a nonconvex optimal resource allocation model, and its stability with a local minimizer is proven. Further, a nonconvex optimal resource allocation model subject to "on/off" constraints is introduced. Based on an augmented Lagrangian function, another penalty-function-type multi-agent system is developed, and it is proven to be stable with a local minimizer. A numerical example with simulation in a heating, ventilation, and air conditioning system is presented to demonstrate the theoretical results.

Although the performance of a differential chaos shift keying (DCSK) system can be improved by designing appropriate channel codes, it is still a great challenge to do so for various channel models through transmitter and receiver designs, particularly with short frame length transmissions. Alternatively, to bypass the difficult task of designing a good channel code, an iterative receiver (IR) DCSK system is proposed in this paper. A soft demapper suitable for non-coherent M-ary DCSK system is presented, which can work either with or without fading amplitude information. Bit error rate (BER) performance shows its advantages over the non-IR DCSK system in different channel models. The achievable rate curves also indicate that gains relative to non-iterative DCSK system can be achieved with the iterative receiver.

The influence of structural properties of a network on the network synchronizability is studied by introducing a new concept of average range of edges. For both small-world and scale-free networks, the effect of average range on the synchronizability of networks with bounded or unbounded synchronization regions is illustrated through numerical simulations. The relations between average range, range distribution, average distance, and maximum betweenness are also explored, revealing the effects of these factors on the network synchronizability of the small-world and scale-free networks, respectively.

No abstract is provided for this article.

This paper proposes several comparable degree-energy-based local random routing strategies for wireless sensor networks and reports a detailed numerical study of the new strategies. Specifically, energy consumption and lifetime efficiency of sensor networks are investigated for five different structural topologies and five different routing methods under the degree-energy-awareness principle, regarding their performances and costs. The new findings could provide some references and guidelines for designing more efficient sensor networks under various conditions for future applications. Based on these findings, a few rules of thumb for the design of more energy-efficient sensor networks are suggested.

In this paper, a coordinated tracking control problem for a multi-agent system under noise perturbations is considered over a leader-follower communication network. Two control gain functions are designed where the first one is used to attenuate the effect of noise and the second to stabilize the system. Using tools from the algebraic graph theory and stochastic analysis, it is proved that the followers can track the leader in mean square over a time-invariant network. The results are then extended to the scenario of a time-varying network.