In our previous work, we proposed a novel non-orthogonal multiple access (NOMA) scheme with high dimensional modulation and its user pairing strategy. We derived the power allocation factor based on the upper bound of symbol error probability so that there are about 1 dB difference between two user’s symbol error probability. In this paper, we derive the power allocation factor by the approximation bound to achieve more accurate symbol error probability and reduce the gap. Numerical results show that the gap between two user’s symbol error probabilities can be very small by setting reasonable parameters.

The plant-wide production process is composed of multiple unit processes, in which the operational indices of each unit process are assigned and adjusted according to product quality, yield, and actual operating modes. Due to the changing operational conditions of the production process, the operational indices cannot be effectively adjusted by most of the model-based methods or evolutionary computation. In this article, the decision making of operational indices is formulated as a continuous state, continuous action reinforcement learning (RL) problem and a model-free RL algorithm is proposed, which learns a decision policy to determine the operational indices according to the actual operational conditions. Different from the existing methods, this article presents a multiactor networks ensemble algorithm and an actor-critic framework with stochastic policy to avoid falling into local optimums. The relatively overall optimal policy is obtained by extracting the results of parallel training of multiactor networks, which guarantees the optimality of the obtained policy. In addition, by using the experience replay, it is particularly valuable to effectively deal with the problem that lacking of sampling data in the model-free RL. Simulation studies are conducted on actual data of a mineral processing plant and the results demonstrate the effectiveness of the proposed algorithm.

Deep learning (DL) defines a new data-driven programming paradigm where the internal system logic is largely shaped by the training data. The standard way of evaluating DL models is to examine their performance on a test dataset. The quality of the test dataset is of great importance to gain confidence of the trained models. Using an inadequate test dataset, DL models that have achieved high test accuracy may still lack generality and robustness. In traditional software testing, mutation testing is a well-established technique for quality evaluation of test suites, which analyzes to what extent a test suite detects the injected faults. However, due to the fundamental difference between traditional software and deep learning-based software, traditional mutation testing techniques cannot be directly applied to DL systems. In this paper, we propose a mutation testing framework specialized for DL systems to measure the quality of test data. To do this, by sharing the same spirit of mutation testing in traditional software, we first define a set of source-level mutation operators to inject faults to the source of DL (i.e., training data and training programs). Then we design a set of model-level mutation operators that directly inject faults into DL models without a training process. Eventually, the quality of test data could be evaluated from the analysis on to what extent the injected faults could be detected. The usefulness of the proposed mutation testing techniques is demonstrated on two public datasets, namely MNIST and CIFAR-10, with three DL models.

Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data. We have seen wide adoption of DL in many safety-critical scenarios. However, a plethora of studies have shown that the state-of-the-art DL systems suffer from various vulnerabilities which can lead to severe consequences when applied to real-world applications. Currently, the testing adequacy of a DL system is usually measured by the accuracy of test data. Considering the limitation of accessible high quality test data, good accuracy performance on test data can hardly provide confidence to the testing adequacy and generality of DL systems. Unlike traditional software systems that have clear and controllable logic and functionality, the lack of interpretability in a DL system makes system analysis and defect detection difficult, which could potentially hinder its real-world deployment. In this paper, we propose DeepGauge, a set of multi-granularity testing criteria for DL systems, which aims at rendering a multi-faceted portrayal of the testbed. The in-depth evaluation of our proposed testing criteria is demonstrated on two well-known datasets, five DL systems, and with four state-of-the-art adversarial attack techniques against DL. The potential usefulness of DeepGauge sheds light on the construction of more generic and robust DL systems.