Cognitive Computation and Systems最新文献

Gas-insulated switchgear (GIS) is an important power equipment. The implementation of health monitoring is limited by the number of sensors, and the global detection results of the system should be highly credible to ensure the reliability of the power supply system. To solve this problem, this study proposes a sensor layout optimization method based on global detection probability performance evaluation. Starting from the cost function, the GIS discharge detection problem is transformed into a Bayesian risk decision problem, the binary state of ‘with discharge’ and ‘without discharge’ is adopted to simplify the cost function and reduce the computing workload, and the objective function representing the global detection performance of the system is obtained. The solution of layout optimization is realized by the improved genetic algorithm. 3-sensor, 4-sensor and 6-sensor layouts, which are digitally simulated at different detection rates, and then the distribution diagram of the global detection rate is obtained. On this basis, the feasibility and effectiveness of the optimization method are verified through an experiment. The results show that, compared with other sensor layout optimization methods, this optimization method can obtain the correct probability distribution of the detection rate globally and realize the graphical quantization of the detection performance distribution of the system so as to ensure the system performance.

In order to solve the problem of low accuracy and efficiency of soil moisture content prediction in tea plantations and improve the level of soil water content prediction, a soil moisture content prediction model for tea plantations based on the support vector machine (SVM)-optimised bald eagle search (BES) algorithm (BES-SVM) is proposed. Soil data and environmental data of tea plantations were transmitted to the server using sensor nodes and weather station nodes. The prediction models of soil moisture content and natural environmental parameters such as soil electrical conductivity, soil temperature, air temperature, air humidity, light intensity, and rainfall were developed using the SVM model optimised by the bald eagle search algorithm, and the mean square error (MSE) and coefficient of determination ($��R^2�$) were calculated to evaluate the model performance. Meanwhile, the performance of the BES-SVM model is compared with the particle swarm algorithm optimisation SVM (PSO-SVM) and genetic algorithm optimised SVM (GA-SVM) models. Results show that the proposed model has a mean coefficient of determination of 95.65%, and the prediction performance is better than the PSO-SVM and GA-SVM model, indicating that the BES-SVM model has good performance and is a feasible prediction method for soil water content prediction and guiding irrigation and fertilisation management in tea plantations.

The Hipster Effect is a group of evolutionary ‘‘Diffusive Learning’’ processes of networks of individuals and groups (and their communication devices) that form Cyber-Physical Systems; and the Hipster Effect theory has potential applications in many fields of research. This study addresses decision-making parameters in machine-learning algorithms, and more specifically, critiques the explanations for the Hipster Effect, and discusses the implications for portfolio management and corporate bankruptcy prediction (two areas where AI has been used extensively). The methodological approach in this study is entirely theoretical analysis. The main findings are as follows: (i) the Hipster Effect theory and associated mathematical models are flawed; (ii) some decision-making and learning models in machine-learning algorithms are flawed; (iii) but regardless of whether or not the Hipster Effect theory is correct, it can be used to develop portfolio management models, some of which are summarised herein; (iv) the [1] corporate bankruptcy prediction model can also be used for portfolio-selection (stocks and bonds).

The extreme learning machine-based autoencoder (ELM-AE) has attracted a lot of attention due to its fast learning speed and promising representation capability. However, the existing ELM-AE algorithms only reconstruct the original input and generally ignore the probability distribution of the data. The minimum error entropy (MEE), as an optimal criterion considering the distribution statistics of the data, is robust in handling non-linear systems and non-Gaussian noises. The MEE is equivalent to the minimisation of the Kullback–Leibaler divergence. Inspired by these advantages, a novel randomised AE is proposed by adopting the MEE criterion as the loss function in the ELM-AE (in short, the MEE-RAE) in this study. Instead of solving the output weight by the Moore–Penrose generalised inverse, the optimal output weight is obtained by the fixed-point iteration method. Further, a quantised MEE (QMEE) is applied to reduce the computational complexity of. Simulations have shown that the QMEE-RAE not only achieves superior generalisation performance but is also more robust to non-Gaussian noises than the ELM-AE.

With the aim to identify new fault diagnosis and advanced robotic systems, this paper first proposes a fault diagnosis algorithm based on an artificial immune network model that can adjust the pruning threshold. Secondly, the algorithm is improved based on neighbourhood rough set theory, in which the relationships among the pruning threshold, misdiagnosis rate, and missed diagnosis rate in the shape space are discussed. In addition, an improved algorithm for adjusting the adaptively pruning threshold based solely on an observation index is described. The simulation experiments show that the algorithm can identify the new fault modes while keeping the misdiagnosis and missed diagnosis rates low.

The creation of artificial moral systems requires making difficult choices about which of varying human value sets should be instantiated. The industry-standard approach is to seek and encode moral consensus. Here the authors' argue, based on evidence from empirical psychology, that encoding current moral consensus risks reinforcing current norms, and thus inhibiting moral progress. However, so do efforts to encode progressive norms. Machine ethics is thus caught between a rock and a hard place. The problem is particularly acute when progress beyond prevailing moral norms is particularly urgent, as is currently the case due to the inadequacy of prevailing moral norms in the face of the climate and ecological crisis.

We find ourselves at a unique point of time in history. Following over two millennia of debate amongst some of the greatest minds that ever existed about the nature of morality, the philosophy of ethics and the attributes of moral agency, and after all that time still not having reached consensus, we are coming to a point where artificial intelligence (AI) technology is enabling the creation of machines that will possess a convincing degree of moral competence. The existence of these machines will undoubtedly have an impact on this age old debate, but we believe that they will have a greater impact on society at large, as AI technology deepens its integration into the social fabric of our world. The purpose of this special issue on Computing Morality is to bring together different perspectives on this technology and its impact on society. The special issue contains four very different and inspiring contributions.

A conventional robot programming method extensively limits the reusability of skills in the developmental aspect. Engineers programme a robot in a targeted manner for the realisation of predefined skills. The low reusability of general-purpose robot skills is mainly reflected in inability in novel and complex scenarios. Skill transfer aims to transfer human skills to general-purpose manipulators or mobile robots to replicate human-like behaviours. Skill transfer methods that are commonly used at present, such as learning from demonstrated (LfD) or imitation learning, endow the robot with the expert's low-level motor and high-level decision-making ability, so that skills can be reproduced and generalised according to perceived context. The improvement of robot cognition usually relates to an improvement in the autonomous high-level decision-making ability. Based on the idea of establishing a generic or specialised robot skill library, robots are expected to autonomously reason about the needs for using skills and plan compound movements according to sensory input. In recent years, in this area, many successful studies have demonstrated their effectiveness. Herein, a detailed review is provided on the transferring techniques of skills, applications, advancements, and limitations, especially in the LfD. Future research directions are also suggested.

VirtuosA (‘virtuous algorithm’) is introduced, a model in which artificial intelligence (AI) systems learn ethical behaviour based on a framework adapted from Christian philosopher Dallas Willard and brought together with associated neurobiological structures and broader systems thinking. To make the inquiry concrete, the authors present a simple example scenario that illustrates how a robot might acquire behaviour akin to the virtue of kindness that can be attributed to humans. References to philosophical work by Peter Sloterdijk help contextualise Willard’s virtue ethics framework. The VirtuosA architecture can be implemented using state-of-the-art computing practices and plausibly redescribes several concrete scenarios implemented from the computing literature and exhibits broad coverage relative to other work in ethical AI. Strategies are described for using the model for systems evaluation —particularly the role of ‘embedded evaluation’ within the system—and its broader application as a meta-ethical device is discussed.