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This paper aims to realize a fully distributed multi-UAV collision detection and avoidance based on deep reinforcement learning (DRL). To deal with the problem of low sample efficiency in DRL and speed up the training. To improve the applicability and reliability of the DRL-based approach in multi-UAV control problems.

In this paper, a fully distributed collision detection and avoidance approach for multi-UAV based on DRL is proposed. A method that integrates human experience into policy training via a human experience-based adviser is proposed. The authors propose a hybrid control method which combines the learning-based policy with traditional model-based control. Extensive experiments including simulations, real flights and comparative experiments are conducted to evaluate the performance of the approach.

A fully distributed multi-UAV collision detection and avoidance method based on DRL is realized. The reward curve shows that the training process when integrating human experience is significantly accelerated and the mean episode reward is higher than the pure DRL method. The experimental results show that the DRL method with human experience integration has a significant improvement than the pure DRL method for multi-UAV collision detection and avoidance. Moreover, the safer flight brought by the hybrid control method has also been validated.

The fully distributed architecture is suitable for large-scale unmanned aerial vehicle (UAV) swarms and real applications. The DRL method with human experience integration has significantly accelerated the training compared to the pure DRL method. The proposed hybrid control strategy makes up for the shortcomings of two-dimensional light detection and ranging and other puzzles in applications.

This paper aims to study the working principle of the capacitive pressure sensor and explore the distribution of pressure acting on the surface of the capacitor. Herein, a kind of high sensitivity capacitive pressure sensor was prepared by overlaying carbon fibers (CFs) on the surfaces of the thermoplastic elastomer (TPE), the TPE with high elasticity is a dielectric elastomer for the sensor and the CFs with excellent electrical conductivity were designed as the conductor.

Due to the excellent mechanical properties and electrical conductivity of CFs, it was designed as the conductor layer for the TPE/CFs capacitive pressure sensor via laminating CFs on the surfaces of the columnar TPE. Then, a ‘#' type structure of the capacitive pressure sensor was designed and fabricated.

The ‘#' type of capacitive pressure sensor of TPE/CFs composite was obtained in high sensitivity with a gauge factor of 2.77. Furthermore, the change of gauge factor values of the sensor under 10 per cent of applied strains was repeated for 1,000 cycles, indicating its outstanding sensing stability. Moreover, the ‘#' type capacitive pressure sensor of TPE/CFs was consisted of several capacitor arrays via laminating CFs, which could detect the distribution of pressure.

The TPE/CFs capacitive pressure sensor was easily fabricated with high sensitivity and quick responsiveness, which is desirably applied in wearable electronics, robots, medical devices, etc.

The outcome of this study will help to fabricate capacitive pressure sensors with high sensitivity and outstanding sensing stability.

The purpose of this paper is to present a coordinated control strategy for stable walking of biped robot with heterogeneous legs (BRHL), which consists of artificial leg (AL) and intelligent bionic leg (IBL).

The original concentrated control in common biped robot system is replaced by a master‐slave dual‐leg coordinated control. P‐type open/closed‐loop iterative learning control is used to realize the time‐varying gait tracking for IBL to AL.

The new control architecture can simplify gait planning scheme of BRHL system with complicated closed‐chain mechanism and mixed driving mode.

Designing and constructing a suitable magneto‐rheological damper can greatly improve the control performance of IBL.

Master‐slave coordination strategy is suitable for BRHL stable walking control.

The concepts and methods of dual‐leg coordination have not been explicitly proposed in single biped robot control research before. Master‐slave coordinated control strategy is suitable for complicated BRHL.

Despite an intensified anti-corruption campaign, China's economic growth and social transition continue to breed loopholes and opportunities for big corruption, leading to a money-oriented mentality and the collapse of ethical standards, and exposing the communist regime to greater risk of losing moral credibility and political trust. In Hong Kong, the setting up of the Independent Commission Against Corruption (ICAC) in 1974 marked the advent of a new comprehensive strategy to eradicate corruption and to rebuild trust in government. The ICAC was not just an anti-corruption enforcement agency per se, but an institution spearheading and representing integrity and governance transformation. This chapter considers how mainland China can learn from Hong Kong's experience and use the fight against corruption as a major political strategy to win the hearts and minds of the population and reform governance in the absence of more fundamental constitutional reforms, in a situation similar to Hong Kong's colonial administration of the 1970s–1980s deploying administrative means to minimize a political crisis.

Anomaly detection of network attacks has become a high priority because of the need to guarantee security, privacy and reliability. This work aims to describe both intelligent immunological approaches and traditional monitoring systems for anomaly detection.

Author investigated different artificial immune system (AIS) theories and proposes how to combine different ideas to solve problems of network security domain. An anomaly detection system that applies those ideas was built and tested in a real time environment, to test the pros and cons of AIS and clarify its applicability. Rather than building a detailed signature based model of intrusion detection system, the scope of this study tries to explore the principle in an immune network focusing on its self-organization, adaptive learning capability, and immune feedback.

The natural immune system has its own intelligent mechanisms to detect the foreign bodies and fight them and without it, an individual cannot live, even just for several days. Network attackers evolved new types of attacks. Attacks became more complex, severe and hard to detect. This results in increasing needs for network defense systems, especially those with ability to extraordinary approaches or to face the dynamic nature of continuously changing network threats. KDD CUP'99 dataset are used as a training data to evaluate the proposed hybrid artificial immune principles anomaly detection. The average cost of the proposed model was 0.1195 where that the wining of KDD99 dataset computation had 0.233.

It is original to introduce investigation on the vaccination biological process. A special module was built to perform this process and check its usage and how it could be formulated in artificial life.