Dual-population Co-evolution Multi-objective Optimization Algorithm and Its Application

In the multi-objective optimization algorithm, the parameter strategy has a huge impact on the performance of the algorithm, and it is difficult to set a set of parameters with excellent distribution and convergence performance in the actual optimization process. Based on the MOEA/D algorithm framework, this paper construct an improved dual-population co-evolution MOEA/D algorithm by adopt the idea of dual-population co-evolution. The simulation test of the benchmark functions shows that the proposed dual-population co-evolution MOEA/D algorithm have significant improvements in IGD and HV indicators compare with three other comparison algorithms. Finally, the application of the LTE base station power allocation model also verifies the effectiveness of the proposed algorithm.

An Interoperable Framework for Computational Models of Emotion

Computational models of emotion (CMEs) are software systems designed to emulate specific aspects of the human emotions process. The underlying components of CMEs interact with cognitive components of cognitive agent architectures to produce realistic behaviors in intelligent agents. However, in contemporary CMEs, the interaction between affective and cognitive components occurs in ad-hoc manner, which leads to difficulties when new affective or cognitive components should be added in the CME. This paper presents a framework that facilitates taking into account in CMEs the cognitive information generated by cognitive components implemented in cognitive agent architectures. The framework is designed to allow researchers define how cognitive information biases the internal workings of affective components. This framework is inspired in software interoperability practices to enable communication and interpretation of cognitive information and standardize the cognitive-affective communication process by ensuring semantic communication channels used to modulate affective mechanisms of CMEs

Citrus Huanglongbing Recognition Algorithm Based on CKMOPSO

In view of the similarity of characteristics between the features of the disease images and the large dimension, and the features correlation of the disease images, this will lead to the generation of feature redundancy, and will introduce a serious impact on the recognition efficiency and accuracy of citrus Huanglongbing. In addition, they have the defects of high cost of detection algorithms and low detection accuracy. This will occur in the image cutting feature extraction stage, so this paper uses the citrus Huanglongbing recognition algorithm based on kriging model simplex crossover local based search Multi-objective particle swarm optimization algorithm(CKMOPSO) selects feature vectors with strong classification capabilities from the original disease image features, experimental results show that this is an effective recognition method.

S-Box Construction Method Based on the Combination of Quantum Chaos and PWLCM Chaotic Map

For a security system built on symmetric-key cryptography algorithms, the substitution box (S-box) plays a crucial role to resist cryptanalysis. In this article, we incorporate quantum chaos and PWLCM chaotic map into a new method of S-box design. The secret key is transformed to generate a six tuple system parameter, which is involved in the generation process of chaotic sequences of two chaotic systems. The output of one chaotic system will disturb the parameters of another chaotic system in order to improve the complexity of encryption sequence. S-box is obtained by XOR operation of the output of two chaotic systems. Over the obtained 500 key-dependent S-boxes, we test the S-box cryptographical properties on bijection, nonlinearity, SAC, BIC, differential approximation probability, respectively. Performance comparison of proposed S-box with those chaos-based one in the literature has been made. The results show that the cryptographic characteristics of proposed S-box has met our design objectives and can be applied to data encryption, user authentication and system access control.

Controller Design for Temperature Control of MISO Water Tank System

This article introduces the design of split range control and fuzzy logic control for temperature control of the MISO (multiple input single output) water tank scheme. A multiple input single output (MISO) system is considered for the proposed work as most of the practical systems comprise of numerous MISO system. Investigations are conducted on the impact of control parameters, system dynamics and process disturbances. From the simulation outcomes, it is clearly inferred that the fuzzy logic controller outperformed split range control over all parameters.

Relatively-Integrated Ship Navigation by H¥ Fusion Filters

For the system with unknown statistical property noises, the property that the energies of the system noise and the observation noise are limited is utilized in this paper. On this basis, two novel fusion algorithms are proposed for ship integrated navigation with the relative navigation information, broadcasted by the Automatic Identification Systems (AISs) in the adjacent ships. Firstly, an H∞ fusion filtering algorithm is given to deal with the navigation observation messages, under the centralized fusion framework. The integrated navigation method based on this algorithm cannot deal with the asynchronous navigation messages in real time. Therefore, a sequential H∞ fusion filtering algorithm is also given to sequentially deal with the asynchronous navigation messages, secondly. Finally, a computer simulation is employed to illustrate the validity and feasibility of the sequential method.

Cognitive Support Tools for a Pre-Performance Routine in a Darts Game

This paper describes two types of a cognitive support tool for a pre-performance routine (PPR) in darts game. PPRs entail the performance of determined motions before an action and are often executed in sports for the purpose of removing stress or raising concentration. The concentration-stabilizing phenomenon was discovered by the previous research and it determined that the phenomenon appears more conspicuous in the case of experts and PPRs. A tool using a simple brainwaves sensor has been designed and shows us the current status of concentration and notifies us of the concentration-stabilizing phenomenon on a tablet computer. Another tool has been developed on a smart watch with a heart rate sensor. The smart watch indicated heartbeat as a “beep” sound to a user. It was designed based on a result that indicated that darts game scores tend to improve by throwing immediately after a heartbeat. The effectiveness of the tools was verified in several experiments.

Convolutional Neural Network Integrated With Fuzzy Rules for Decision Making in Brain Tumor Diagnosis

Conventional methods used in brain tumors detection, diagnosis, and classification such as magnetic resonance imaging and computed tomography scanning technologies are unbridged in their results. This paper presents a proposed model combination, convolutional neural networks with fuzzy rules in the detection and classification of medical imaging such as healthy brain cell and tumors brain cells. This model contributes fully on the automatic classification and detection medical imaging such as brain tumors, heart diseases, breast cancers, HIV and FLU. The experimental result of the proposed model shows overall accuracy of 97.6%, which indicates that the proposed method achieves improved performance than the other current methods in the literature such as [classification of tumors in human brain MRI using wavelet and support vector machine 94.7%, and deep convolutional neural networks with transfer learning for automated brain image classification 95.0%], uses in the detection, diagnosis, and classification of medical imaging decision supports.

Application of an Encoding Revision Algorithm in Overlapping Coalition Formation

Overlapping coalition formation is a very active research field in multi-agent systems (MAS). In overlapping coalition, each agent can participate in different coalitions corresponding to multiple tasks at the same time. As each agent has limited resources, resource conflicts will occur. In order to resolve resource conflicts, we develop an improved encoding revision algorithm in this paper which can revise an invalid two-dimensional binary encoding into a valid one by checking the encoding for each row. To verify the effectiveness of the algorithm, differential evolution was used as the experimental platform and compared with Zhang et al. The experimental results show that the algorithm in this paper is superior to Zhang et al. in both solution quality and encoding revision time.

Arabic Authorship Attribution Using Synthetic Minority Over-sampling Technique and Principal Components Analysis for Imbalanced Documents

Nowadays, dealing with imbalanced data represents a great challenge in data mining as well as in machine learning task. In this investigation, we are interested in the problem of class imbalance in Authorship Attribution (AA) task, with specific application on Arabic text data. This article proposes a new hybrid approach based on Principal Components Analysis (PCA) and Synthetic Minority Over-sampling Technique (SMOTE), which considerably improve the performances of authorship attribution on imbalanced data. The used dataset contains 7 Arabic books written by 7 different scholars, which are segmented into text segments of the same size, with an average length of 2900 words per text. The obtained results of our experiments show that the proposed approach using the SMO-SVM classifier, presents high performance in terms of authorship attribution accuracy (100%), especially with starting character-bigrams. In addition, the proposed method appears quite interesting by improving the AA performances in imbalanced datasets, mainly with function words.