Cognitive Computation and Systems最新文献

With the international development of the medical service informatisation, medical information sharing has become the key standard to measure the degree of medical informatisation. In this process, it is important to ensure the security of patients' medical information such as patients' records, examination reports, images and so on. In this article, an image encryption scheme based on Secure Hash Algorithm 3 (SHA-3), DNA coding and high dimensional chaos system is proposed to promote the security level of medical images in information sharing processes such as over the network. First, SHA-3 algorithm is used to calculate the hash value of the input image, and the result is taken as the initial value of the hyper-chaotic system. Second, the intensity value of the input image is converted into a serial binary digital stream. Third, the pseudo-random sequence generated by a 4-dimensional hyper-chaotic system is used to perturb the bit stream globally so as to achieve the purpose of hiding the effective information of the input image. During the operation of hyper-chaotic sequence and DNA sequence, algebraic and complementary operations are performed on DNA encoding values to enhance encryption performance. Finally, simulations have been applied on an open-source medical image database, and the results demonstrate obvious encryption effectiveness and high security level of the proposed encryption algorithm in the robustness of noise and clipping attacks.

The problem of optimal dispatching of a power system containing a high proportion of renewable energy is of great significance for the realisation of new energy consumption and the economic and reliable operation of the power system. For the solution of non-linear, non-convex, multi-objective problems for the optimal operation design of a power system with wind and photovoltaic access, traditional methods have difficulties in terms of computational real-time and iterative convergence. To address this issue, a deep reinforcement learning-based optimal scheduling method for the hybrid power system is proposed, which enables continuous action control to obtain an optimal scheduling strategy through the interaction between the agent and the hybrid power system. Firstly, a mathematical description of the optimal scheduling problem containing wind power and photovoltaic power system is presented, and the state space, action space, and reward function of the agent are designed. Secondly, the basic framework of the deep reinforcement learning optimal scheduling model is constructed, and the basic principles of the twin delayed deep deterministic policy gradient algorithm are introduced. Finally, the effectiveness of the deep reinforcement learning model for day-ahead optimal scheduling of the hybrid power system is verified by means of an arithmetic analysis of the modified New England 39-bus system.

Based on the characteristics of human cognition and the cloud model, the excursion of uncertain concepts by the similarity between uncertain concepts from the perspective of conceptual cognition is studied. Firstly, considering the different meanings of the numerical characters in cloud concept, the properties of cloud concepts similarity are given. Furthermore, in order to reflect the various similarities that may exist in uncertain concept, five similarities between cloud concepts are constructed, specifically include the similarity between concept expectations, the similarity between concept entropy, the similarity between concept hyper entropy, the shape similarity and the overall similarity. Secondly, the rationality of these proposed similarity measurements is illustrated by comparing with the existing methods through the specific data analysis. Finally, two cognitive experiments, including concept cognitive processes without prior knowledge and concept cognitive processes with prior knowledge (including positive prior knowledge and negative prior knowledge), are designed. These experiments are all used to study the excursion in the process of concept cognition by the similarity of cloud concepts. The experiment results show that the proposed similarities are reasonable by comparing the proposed method with the existing ones, and the effectiveness of the proposed method is also verified by the excursion of concept cognition.

Prostate brachytherapy is a validated treatment for prostate cancer. During the procedure, the accuracy of needle placement is critical to the treatment’s effectiveness. However, the inserted needle could deflect from the preset trajectory because of the needle deflection, tissue shifting caused by the interaction between the needle and soft tissue, as well as the effects of pre-inserted needles. There are significant challenges in needle placement areas, especially in prostate brachytherapy, because multiple needles are required for the effectiveness of radiation. To overcome these limitations, relevant research is carried out in mechanical, computer science, and material science areas. With the development of surgical robotics, researchers are also exploring the possibilities of raising the accuracy of needle placement with surgical-assisted robotics. This study provides a review over the last 3 decades in each of the component research areas that constitutes a surgical robotics system, including needle steering approaches, needle-tissue deformation models, path planning algorithms and different automatic level surgical robotics systems used for prostate cancer treatment, especially prostate brachytherapy. Further directions for researchers are also suggested.

Aiming to help improve quality of life of the visually impaired people, this paper presents a novel wearable aid in the shape of a helmet for helping them find objects in indoor scenes. An object-goal navigation system based on a wearable device is developed, which consists of four modules: object relation prior knowledge (ORPK), perception, decision and feedback. To make the aid also work well in unfamiliar environment, ORPK is used for sub-goal inference to help the user find the target goal. And a method that learns the ORPK from unlabelled images by utilising a scene graph and knowledge graph is proposed. The effectiveness of the aid is demonstrated in real world experiments.

Face detection is the basic step of many face analysis tasks. In practice, face detectors usually run on mobile devices with limited memory and computing resources. Therefore, it is important to keep face detectors lightweight. To this end, current methods usually focus on directly designing lightweight detectors. Nevertheless, it is not fully explored whether the resource consumption of these lightweight detectors can be further suppressed without too much sacrifice on accuracy. In this study, we propose to apply the network pruning method to the lightweight face detection network, to further reduce its parameters and floating point operations. To identify the channels of less importance, we perform network training with sparsity regularisation on channel scaling factors of each layer. Then, we remove the connections and corresponding weights with near-zero scaling factors after sparsity training. We apply the proposed pruning pipeline to a state-of-the-art face detection method, EagleEye, and get a shrunken EagleEye model, which has a reduced number of computing operations and parameters. The shrunken model achieves comparable accuracy as the unpruned model. By using the proposed method, the shrunken EagleEye achieves a 56.3% reduction of parameter size with almost no accuracy loss on the WiderFace dataset.

Engineers solving engineering design problems can be regarded as a gradual optimisation process that involves strategising. The process can be modelled as a reinforcement learning (RL) framework. This article presents an RL model with episodic controllers to solve engineering problems. Episodic controllers provide a mechanism for using the short-term and long-term memories to improve the efficiency of searching for engineering problem solutions. This work demonstrates that the two kinds of models of memories can be incorporated into the existing RL framework. Finally, an optimised design problem of a crane girder is illustrated by RL with episodic controllers. The work presented in this study leverages the RL model that has been shown to mimic human problem solving in engineering optimised design problems.

In order to solve the problem that the existing methods cannot effectively capture the semantic emotion of the sentence when faced with the lack of cross-language corpus, it is difficult to effectively perform cross-language sentiment analysis, we propose a neural network architecture called the Attention-Based Hybrid Robust Neural Network. The proposed architecture includes pre-trained word embedding with fine-tuning training to obtain prior semantic information, two sub-networks and attention mechanism to capture the global semantic emotional information in the text, and a fully connected layer and softmax function to jointly perform final emotional classification. The Convolutional Neural Networks sub-network captures the local semantic emotional information of the text, the BiLSTM sub-network captures the contextual semantic emotional information of the text, and the attention mechanism dynamically integrates the semantic emotional information to obtain key emotional information. We conduct experiments on Chinese (International Conference on Natural Language Processing and Chinese Computing) and English (SST) datasets. The experiment is divided into three subtasks to evaluate the superiority of our method. It improves the recognition accuracy of single sentence positive/negative classification from 79% to 86% in the single-language emotion recognition task. The recognition performance of fine-grained emotional tags is also improved by 9.6%. The recognition accuracy of cross-language emotion recognition tasks has also been improved by 1.5%. Even in the face of faulty data, the performance of our model is not significantly reduced when the error rate is less than 20%. These experimental results prove the superiority of our method.

The article presents the design and development of a virtual fretless Chinese stringed instrument App with the Duxianqin as an example, whose performance is expected to be no different from a real instrument. The digital simulation of fretless musical instruments is mainly divided into two parts: the simulation of the continuous pitch processing of the strings, and the simulation of the sound produced by plucking strings. The article returns to the theory of mechanics and wave theory and obtains the quantitative relationship between string frequency and its deformation and elongation. The Duxianqin selected in this article is a fretless instrument, which cannot be completely simulated by relying solely on sound source data. Playing and vocalization require real-time synthesis through pitch processing, which has certain reference significance for the realization of other fretless instruments.