International Journal of Reasoning-based Intelligent Systems

Tyugashev, Andrey A.

doi: 10.1504/IJRIS.2020.106802pmid: N/A

This article provides the theoretical background and practical case studies of the application of reasoning and other logic-based approaches to the moving objects control. Modern moving objects, both manned and unmanned, utilise computers as their 'onboard brain'. Since planes, spacecraft, cars, trucks and trains must demonstrate flexible and safe behaviour in various situations, it seems prospective to use intelligent control means instead of rigid control logic dispersed in a program source code. This article is concerned with the possible implementation of onboard intelligence. In contrast to the popular use of neural networks, the logic-based approach is based on clear and exact control rules with strict responsibility. Thus, formal specification and verification methods can be utilised. The article describes the real-time control algorithm logic (RTCAL) for the above-mentioned purposes. We also present case studies of reasoning at the design and operation stages for providing the fault tolerant control of a spacecraft.

Wasid, Mohammed; Ali, Rashid

doi: 10.1504/IJRIS.2020.106803pmid: N/A

There have been significant advances made in the research of recommender systems over the past decades and have been implemented in both industry and academia. Recently, multi-criteria ratings are being incorporated into traditional recommender systems to further improve their quality, especially to handle the data sparsity and cold start issues. However, incorporation of multi-criteria ratings have improved the performance of the recommendation, but at the same time, multi-dimensionality issue also arises. This paper presents a clustering-based recommendation approach which is used for dealing with the multi-dimensionality issue in multi-criteria recommender systems. Here, we cluster the users based on their individual criteria ratings using K-means clustering and the intra-cluster similarity is computed using Mahalanobis distance measure for neighbourhood set generation. This improves the recommendations quality and predictive accuracy of both traditional and clusteringbased collaborative recommendations. The Yahoo! Movies dataset was used for testing the approach and the experiment conducted shows promising results.

Zotin, Alexander G.

doi: 10.1504/IJRIS.2020.106804pmid: N/A

In this paper, a fast image enhancement algorithm based on multi-scale retinex in HSV colour model is presented. The proposed algorithm produces the result similar to the one which uses a nonlinear processing in the HSV colour model, but with less computational cost. It uses linear dependencies of RGB colours from the V-component of HSV model. Additionally, to speed up the images processing and enhance the local contrast is suggested to perform multi-scale retinex (MSR) computation only in the low-frequency area obtained by the wavelet transform. Experimental research was performed on more than 100 colour images having non-uniform brightness. Different algorithms based on retinex technology were implemented and their performance was compared. The proposed way of output image colour formation allows reducing processing time by 30%-75%, depending on the image size. The experimental data show that the usage of the wavelet transform in proposed MSR algorithm additionally leads to 2-2.8 times increase in processing speed.

Tang, Xilang; Xiao, Mingqing; Hu, Bin; Pan, Dongqing

doi: 10.1504/IJRIS.2020.106805pmid: N/A

To improve the development efficiency of automatical diagnosis equipment (ADE) and ensure the generality of ADE software, this paper proposes a novel method to exchange deep knowledge of systems under diagnosis (SUD) using ontologies. A general framework of knowledge base combining test information model and diagnosis information model is proposed. The diagnosis information model is decomposed into structure model and function model. The structure model describes the connectivity of adjacent components as well as the structural hierarchy, and the function model describes behaviour of modules by mapping input signals into output signals. Moreover, the method to locate the fault based on the proposed knowledge base is introduced. Finally, a case study for guiding system of passive-radar guidance missile is carried out to illustrate our proposed method. The practice shows that our method can achieve the object well.

Mihaylova, Antonia; Georgieva, Veska; Petrov, Plamen

doi: 10.1504/IJRIS.2020.106806pmid: N/A

Most of the known methods of segmentation of the abdominal organs are not automated for the whole series of images or are semi-automatic and require additional intervention by the user. This is typical for cases where the difference in intensity of the grey level between the subject and the background is small. This paper presents a multistage approach for spleen segmentation from MRI-sequences. It is based on segmentation methods such as active contours without edges and k-mean clustering. The proposed approach consists of some basic stages. The first stage is pre-processing, based on image enhancement and morphological operation. Two atlas models are created, which are used in the initial image to define the initial contour at which the segmentation begins. The proposed approach allows extracting the spleen in the different depth images, which has a variable form and unstable position. The conducted experiments are showing the robustness of the proposed approach. The obtained results demonstrate the effectiveness of the approach for application in screening diagnostics.

Saeed, T.R.; Hatem, Ghufran M.; Sadah, Jafar W. Abdul

doi: 10.1504/IJRIS.2020.106807pmid: N/A

This paper presents a robust clutter classifier based on the neural network to assist the radar receiver by choosing optimal constant false alarm rate where this classifier has been trained for 16 classes, four radar return distribution with different situations. The return radar signal distributions are Rayleigh, Weibull, lognormal and K-distribution, while the situations are, signal, multi-target, closed multi-target, and clutter edge. Multilayer perceptron with back-propagation as a neural network with seven features, mean, variance, mode, kurtosis, skewness, median, and entropy, have been used to classify the return signal. A least mean square error is used to evaluate the classifier performance. The simulation is evaluated for the signal to clutter ration from +35 dB to −35 dB, with 5-20 neurons of the hidden layer, and 60-360 samples. By performing, the optimisation has been gained by using 240 samples and 20 neurons then lead to 98.1% return signal classification.

Aharari, Ari; Yang, Won-Seok

doi: 10.1504/IJRIS.2020.106808pmid: N/A

According to the survey on the actual situation of elderly persons at home or nursing home care, the first item after concerning about disease under treatment is 'weak legs and difficulties to stand from the chair'. Muscle strength further decreases with aging and make feeling burden when standing from chair. Also, people who are suffering from secondary symptoms such as bedsores and keep sitting in a chair for a long time are on the rise. The most burdensome for elderly persons when trying to stand up from the chair is to bear the weight themselves. In this paper, we introduce 'Rakutateru' which is especially designed to support elderly persons to easily stand up from the chair and keep people to more active and independent. We also evaluate the validity of an assist unit which is contained inside the lower part of the Rakutateru surface.