Contents of Volume 31 (2021)

1/2021

1/2021

  • [1] Dai J., He Y.H., Li J.Y. (China)
    An approach for heuristic parallel LDTW distance optimization method with bio-inspired strategy , pp. 1-28

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2021.31.001

    Abstract: Dynamic time warping (DTW) is a classical similarity measure for arbitrary length time series. As an effective improvement of DTW, dynamic time warping under limited warping path length (LDTW) oppresses the long-term pathological alignment problem and allows better flexibility. However, since LDTW increases path lengths, it directly leads to higher time-consuming. In this paper, a new method of similarity sequence measurement (ACO LDTW) is proposed by the parallel computing characteristics of ant colony optimization (ACO) algorithm with bio-inspired strategy and the idea of LDTW path restriction. This algorithm searches the optimal path on the restricted distance matrix by simulating the behavior of ant colony parallel foraging. Firstly, the distance matrix is mapped to the 0 - 1 matrix of grid method, and the search range of ants is limited by the warping path in LDTW. Secondly, the state transition probability function, pheromone initialization and update mechanism of ACO algorithm are adapted. On the basis of ensuring the accurate results, the convergence rate can be effectively improved. The validity of ACO LDTW is verified by cases. In the 22 data sets of 1NN classification experiment, ACO LDTW has the lowest classification error rate in 16 data sets, and it is shorter than the calculation time of LDTW. At the same time, it is applied to the field of mechanical fault diagnosis and has the ability to solve practical engineering applications. Experiments show that ACO LDTW is more effective in terms of accuracy and computation time.

  • [2] Samson A.B.P., Chandra S.R.A., Manikant M. (India)
    A deep neural network approach for the prediction of protein subcellular localization , pp. 29-45

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2021.31.002

    Abstract: The subcellular localization of proteins is an essential characteristic of human cells, which plays a vital part in understanding distinct functions and cells’ biological processes. The abnormal protein subcellular localization affects protein functionality and may cause many human diseases ranging from metabolic disorders to cancer. Therefore, the prediction of subcellular locations of the proteins is an important task. Artificial neural network has become a popular research topic in machine learning that can achieve remarkable results in learning highlevel latent traits. This paper proposes a deep neural network (DNN) model to predict the human protein subcellular locations. The DNN automatically learns high-level representations of abstract features and proteins by examining nonlinear relationships between different subcellular locations. The experimental results have shown that the proposed method gave better results compared with the classical machine learning techniques such as support vector machine and random forest. This model also outperformed the similar model, which uses stacked auto-encoder (SAE) with a softmax classifier.

  • [3] Garlík B. (CZ)
    Modelling and optimization of an intelligent environmental energy system in an intelligent area, pp. 47-76

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2021.31.003

    Abstract: The article deals with the current state of energy consumption, the development of distribution networks in the context of its decentralization and integrated community energy systems. The article focuses on the issue and optimization of the operation of EnergyHubs (EH) – energy centres in terms of solving environmental aspects using a mathematical model in the GAMS environment. The acquired knowledge and results of simulations were then applied to a specific urban area to find the optimal variant of EH. The aim of the research is to present its results at the level of cleaner production, improvement of the environment, significant reduction of CO2 and sustainability of society. My experience proves that the achievement of sustainable development goals represents fundamental gaps in research and practical applications, especially at the level of specific projects. It is mainly the application of insufficient indicators and work methodologies in the design of building projects with almost zero energy consumption. Another shortcoming is the coordination of design procedures and applications of optimization and simulation methods necessary to address the energy performance of buildings or clusters of buildings. In addition, the results show growing expectations about the added value of applying artificial intelligence in meeting sustainable development goals, through new data sources that inevitably enter the energy sustainability design process.

  • [4] Kovanda J., Rulc V. (CZ)
    Pre-crash control strategy of driver assistance system, pp. 77-88

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2021.31.004

    Abstract: The aim of the article is the optimisation process of the ADAS (Advanced Driver Assistance Systems) control. The methodology is based on the classification of ADAS systems according to the situations of unavoidable accidents. The evaluation of expected consequences uses injury biomechanics, which represents the extended definition of HMI (Human-Machine Interaction). The evaluation of injury mechanism and the machine intervention enables to control this process with the target to minimise the consequent injuries. Then the decision making takes new inputs to the control process and it enriches the multiparametric control of the system with the target to minimise the consequences.