Organizing Map Neural Network Research Articles

Dynamic impact of hybrid wind-solar photovoltaic power injection on small signal stability of Nigerian 11kV power system using Self Organizing Map neural network

Power industry reorganization, legislation on greenhouse gas emissions and inadequate conventional generation capacity encourage the idea of incorporating Distributed Generation (DG) into the distribution networks as unconventional sources of power generation. High penetration of DG has further increased the complexity of the analysis of small signal stability because the variability of renewable energy sources affects the stability of power system. Therefore, this research work investigates the effect of wind and solar photovoltaic power injection on small signal stability of Nigerian 11 kV power network. Wind and solar photovoltaic were modelled using probabilistic methods and output power at twenty-one different locations within Nigeria was computed. Using the real and reactive power data of the network, Self-Organizing Map (SOM) neural network was trained and tested for the assessment of the small signal stability of the distribution network before and after the injection of renewable power in python virtual environment. The integration of hybrid wind-solar PV distributed generation at 30 %, 60 %, 90 % and 100 % into the studied 11 kV power distribution network improved its small signal stability by 1.4 %, 2.1 %, 2.5 % and 3.2 % respectively. Moreover, increase in the injection of power gotten from wind and solar sources reduces the oscillation frequency of the system from 1.39 Hz to 1.01 Hz and increases the damping from 4.6 % to 13.2 %. Reactive power increment in the range of 5 %-20 % improved the small signal stability of the system by the range 2.4 %-7 %. This improvement will translate to better power transfer capability of the network.

A Real-Time Digital Twin and Neural Net Cluster-Based Framework for Faults Identification in Power Converters of Microgrids, Self Organized Map Neural Network

In developing distribution networks, the deployment of alternative generation sources is heavily motivated by the growing energy demand, as by environmental and political motives. Consequently, microgrids are implemented to coordinate the operation of these energy generation assets. Microgrids are systems that rely on power conversion technologies based on high-frequency switching devices to generate a stable distribution network. However, disrupting scenarios can occur in deployed systems, causing faults at the sub-component and the system level of microgrids where its identification is an economical and technological challenge. This paradigm can be addressed by having a digital twin of the low-level components to monitor and analyze their response and identify faults to take preventive or corrective actions. Nonetheless, accurate execution of digital twins of low-level components in traditional simulation systems is a difficult task to achieve due to the fast dynamics of the power converter devices, leading to inaccurate results and false identification of system faults. Therefore, this work proposes a fault identification framework for low-level components that includes the combination of Real-Time systems with the Digital Twin concept to guarantee the dynamic consistency of the low-level components. The proposed framework includes an offline trained Self Organized Map Neural Network in a hexagonal topology to identify such faults within a Real-Time system. As a case study, the proposed framework is applied to a three-phase two-level inverter connected to its digital model in a Real-Time simulator for open circuit faults identification.

A nonlinear process monitoring strategy with a 2-phase fault diagnosis approach

The article delves into the development of Statistical Fault Detection and Diagnosis Strategies for an Integrated Steel Plant (ISP) taking into account the nonlinear relationship amongst the monitored Process and Feedstock Characteristics. The strategies being devised is based on Neural Network Fitting model cum Principal Component Analysis based technique (NNF-PCA) and Kernel Principal Component Analysis (KPCA) based technique. For detection of fault(s) Hotelling T2 chart based on (Principal Component Analysis) PCA and KPCA scores were employed and an ensemble strategy amalgamating KPCA and Self Organizing Map neural network has also been proposed for the detection of the out-of-control observations or faults. The article also proposes a 2-phase Fault Diagnosis approach christened as Preliminary Diagnosis phase and Specific Diagnosis phase. The Preliminary Diagnosis phase is based on Pattern Analysis of the control chart monitoring statistic observations and the Specific Diagnosis phase is based on the employment of appropriate Fault Diagnostic Statistic. The Preliminary Diagnosis reveals the broader source of assignable cause for the onset of the fault(s) and the Specific Diagnosis reveals the relative contribution of the individual Process and Feedstock characteristics. An in-depth comparative analysis between the NNF-PCA based strategy and KPCA based strategy w.r.t. three comparative aspects and four comparative parameters were carried out with their findings being duly highlighted which revealed the slight effectiveness of the KPCA based strategy with respect to the NNF-PCA based counterpart.

Cluster-based analysis of COVID-19 cases using self-organizing map neural network and K-means methods to improve medical decision-making

In this study, we utilized unsupervised machine learning techniques to examine the relationship between different symptoms in cases who died of COVID-19 and cases who recovered from it. First, our data was cleared of redundancies, and the ten most important variables were selected using a filter-based technique (extra-tree classifier). Next, we calculated the Silhouette, Davis Boldin (DB), and the mean intra-cluster distance measures to select the optimal number of clusters, then clustered the data using both the K-means and hierarchical clustering based on Self Organizing Map (SOM) neural network. Our results revealed that patients who died of COVID-19 had high mean values in different symptoms, but not all patients with this characteristic necessarily died. Besides, our result indicated that the patient's age is directly related to the hospital duration, and elderly patients are more likely to be assigned to the intensive care unit (ICU). However, the patient's sex has the same distribution in different groups and does not correlate with other symptoms. In conclusion, our results confirmed past studies. Also, this research helps physicians improve medical services by considering other important factors for treating different groups of COVID-19 patients.

Edge detection of noisy digital image using optimization of threshold and self organized map neural network

The purpose of this research is to find a suitable method for detecting the edges of noisy digital images by eliminating the noise effects. The image will be partitioned into equal partitions and the initial threshold of that image partition will be calculated. By applying all these thresholds into the self-organized map (SOM) neural network input optimized for learning and training based optimization algorithm (TLBO), threshold clustering will be performed. The partitioned image will be edge detected by entropy method. Choosing the threshold for image segmentation is of great importance. The mean of the brightness of digital noise images is not a good representative of the initial threshold. Noise causes the mean intensity of the brightness to take distance from the main range of the intensity of the image so the resulting edge detected image will be severely noisy and truncated. By determining the highest frequency of brightness intensity instead of the mean brightness, the above-mentioned weaknesses will be eliminated. This method outperforms many current methods, such as Tsallis entropy, Singh and Kiani and even Canny Edge Detection which demonstrates the effectiveness of the proposed method, In the Table 1 the PSNR of image 5 of the proposed method is 61.4896, but Singh method which is 55.61, Tsallis method which is 53.9234, Kiani method which is 53.9315 the proposed method is less than the other methods.

Performance Modeling of Energy Efficiency for Sensors Deployment in Embedded Wireless Sensor Networks

Present trend of Internet of Things (IoT) and sensors deployment increased in every sectors enormously from last one decade. But the deployment challenges of sensors and their networks with respect to their contextual dynamics and system performance is not much investigated. Hence there is a need to investigate the deployment challenges of sensors supporting the computing system that exactly imitates the phenomenon by understanding the context and other influencing parameters, i.e., to sense the environmental parameter values accurately and precisely from the respective embedded sensor system. In this paper, a methodology is proposed to analyze the performance of embedded Wireless Sensor Networks (eWSNs) with respect to energy efficiency based on sensors deployment. The method involves in clustering the sensor nodes based on distance from the phenomenon and its physical location. Sensors and sensor network lifetime energy consumption for data acquisition is analyzed using Markovian model. Simulation platform for random deployment of sensor nodes along with Self Organizing map neural network for clustering with various cases of sensors deployment, network dynamics and environment are studied to understand the performance of the embedded WSN system for energy efficiency.

Automatic condition monitoring of electromechanical system based on MCSA, spectral kurtosis and SOM neural network

Condition monitoring and fault diagnosis play the most important role in industrial applications. The gearbox system is an essential component of mechanical system in fault identification and classification domains. In this paper, we propose a new technique which is based on the Fast-Kurtogram method and Self Organizing Map (SOM) neural network to automatically diagnose two localized gear tooth faults: a pitting and a crack. These faults could have very different diagnostics; however, the existing diagnostic techniques only indicate the presence of local tooth faults without being able to differentiate between a pitting and a crack. With the aim to automatically diagnose these two faults, a dynamic model of an electromechanical system which is a simple stage gearbox with and without defect driven by a three phase induction machine is proposed, which makes it possible to simulate the effect of pitting and crack faults on the induction stator current signal. The simulated motor current signal is then analyzed by using a Fast-Kurtogram method. Self-organizing map (SOM) neural network is subsequently used to develop an automatic diagnostic system. This method is suitable for differentiating between a pitting and a crack fault.

Topological neural network of combined AE and EN signals for assessment of SCC damage

ABSTRACTStress corrosion cracking (SCC) is a common corrosion form that involves undetected premature failures during service life of structures. Since SCC is a combination of electrochemical and mechanical phenomena, coupled acoustic emission and electrochemical noise techniques were proposed to investigate the evolution of SCC damage tomartensitic stainless steel samples. Tests were carried out using a precipitation hardening martensitic stainless steel in an aqueous MgCl2 environment at 100°C, with an applied mechanical stress equal to the 90% of 0.2% yield strength. The synergistic use of the two non-destructive analysis technique was performed using a synchronisation process. The combination of two advanced multivariate analysis techniques (Principal Component Analysis and Self Organising Map neural network) highlighted damage-sensitive features. Characteristic clusters of variables related to specific damage mechanisms were identified discriminating electrochemical activation processes (i.e. pitting), SCC initiation and propagation until final failure phenomena during SCC.

Self organizing map neural networks approach for lithologic interpretation of nuclear and electrical well logs in basaltic environment, Southern Syria

An approach based on self organizing map (SOM) artificial neural networks is proposed herewith oriented towards interpreting nuclear and electrical well logging data. The well logging measurements of Kodana well in Southern Syria have been interpreted by applying the proposed approach. Lithological cross-section model of the basaltic environment has been derived and four different kinds of basalt have been consequently distinguished. The four basalts are hard massive basalt, hard basalt, pyroclastic basalt and the alteration basalt products- clay. The results obtained by SOM artificial neural networks are in a good agreement with the previous published results obtained by other different techniques. The SOM approach is practiced successfully in the case study of the Kodana well logging data, and can be therefore recommended as a suitable and effective approach for handling huge well logging data with higher number of variables required for lithological discrimination purposes.

CLUSTERING OF HIGH RESOLUTION UAV IMAGERY TO IDENTIFY ESSENTIAL PLANTS USING SOM NEURAL NETWORK

The use of high-resolution remote sensing image data is necessary to distinguish essential plants with other plants. This study uses image data taken using Unmanned Aerial Vehicle (UAV) to identify essential plants especially citronella and kaffir lime. To distinguish the structure of essential plants with other objects used texture features extracted by wavelet daubechies method. The features that have been ekstract, then is grouped based on the proximity feature with the Self Organizing Map (SOM) Neural Network. Thus, objects that have similar features will clump together. The tests were conducted on two groups of data sets, where the first group data consisted of plants, buildings and vacant lots, while the second group data consisted only of plants. The results of testing of the first data group shows that the techniques can recognize the citronella plants among other objects, especially building objects and bare land with purity of 0.862745 and Silhouette Coeficient of 0.5520671. While in the second data group, the value of purity and Silhouette Coeficient decreased to 0.737705 and 0.161028. However, from the test of the second data group still shows that the method used can distinguish citronella crops to other plants.

SOMBI: Bayesian identification of parameter relations in unstructured cosmological data

This work describes the implementation and application of a correlation determination method based on Self Organizing Maps and Bayesian Inference (SOMBI). SOMBI aims to automatically identify relations between different observed parameters in unstructured cosmological or astrophysical surveys by automatically identifying data clusters in high-dimensional datasets via the Self Organizing Map neural network algorithm. Parameter relations are then revealed by means of a Bayesian inference within respective identified data clusters. Specifically such relations are assumed to be parametrized as a polynomial of unknown order. The Bayesian approach results in a posterior probability distribution function for respective polynomial coefficients. To decide which polynomial order suffices to describe correlation structures in data, we include a method for model selection, the Bayesian Information Criterion, to the analysis. The performance of the SOMBI algorithm is tested with mock data. As illustration we also provide applications of our method to cosmological data. In particular, we present results of a correlation analysis between galaxy and AGN properties provided by the SDSS catalog with the cosmic large-scale-structure (LSS). The results indicate that the combined galaxy and LSS dataset indeed is clustered into several sub-samples of data with different average properties (for example different stellar masses or web-type classifications). The majority of data clusters appear to have a similar correlation structure between galaxy properties and the LSS. In particular we revealed a positive and linear dependency between the stellar mass, the absolute magnitude and the color of a galaxy with the corresponding cosmic density field. A remaining subset of data shows inverted correlations, which might be an artifact of non-linear redshift distortions.

Rainfall fluctuations and vegetation patterns in alkali grasslands - using self-organizing maps to visualise vegetation dynamics

Knowledge about the drivers of vegetation dynamics in grasslands is fundamental to select appro-priate management for conservation purposes. In this study, we provide a detailed analysis of vegeta-tion dynamics in alkali grasslands, a priority habitat of the Natura 2000 network. We studied vegetation dynamics in five stands of four alkali grassland types in the Hortobagy National Park (eastern Hunga-ry), between 2009 and 2011. We analysed the effect of fluctuations in precipitation on both the overall vegetation composition and on the cover of each species using Self Organizing Map neural networks (SOM). We found that SOM is a promising tool to reveal plant community dynamics. As we analysed species cover and overall vegetation composition separately, we were able to identify the species re-sponsible for particular vegetation changes. Fluctuations in precipitation (a dry season, followed by a wet and an average season) caused quick shifts in plant species composition because of an increasing cover of halophyte forbs, probably because of salinisation. We observed a similar effect of stress from waterlogging in all studied grassland types. The species composition of Puccinellia grasslands was the most stable over the three years with varying precipitation. This was important as this grassland type contained many threatened halophyte species. Self-organising maps revealed small-scale vegetation changes and provided a detailed visualisation of short-term vegetation dynamics, thus we suggest that the application of this method is also promising to reveal community dynamics in more species-rich habitat types or landscapes.

Similarity Analysis of EEG Data Based on Self Organizing Map Neural Network

The Electroencephalography (EEG) is the recording of electrical activity along the scalp. This recorded data are very complex. EEG has a big role in several applications such as in the diagnosis of human brain diseases and epilepsy. Also, we can use the EEG signals to control an external device via Brain Computer Interface (BCI) by our mind. There are many algorithms to analyse the recorded EEG data, but it still remains one of the big challenges in the world. In this article, we extended our previous proposed method. Our extended method uses Self-organizing Map (SOM) as an EEG data classifier. The proposed method we can divide in following steps: capturing EEG raw data from the sensors, applying filters on this data, we will use the frequencies in the range from 0.5~Hz to 60~Hz, smoothing the data with 15-th order of Polynomial Curve Fitting, converting filtered data into text using Turtle Graphic, Lempel-Ziv complexity for measuring similarity between two EEG data trials and Self-Organizing Map Neural Network as a final classifiers. The experiment results show that our model is able to detect up to 96% finger movements correctly.

Study of FIR Filter Designing By Using Self Organizing Map Neural Network

In this paper, designing of a low pass FIR filter is done by artificial neural network. For this kind of application a different type of model is used in ANN. In this research work, selforganizing map (SOM) algorithm is used to train the Neural Network. The SOM algorithm is based on unsupervised learning technique. We also compare the result of neural network with the result of normal window method. The accuracy of SOM neural network is about 98%.

Hybrid Image Compression Method using ANN and DWT

of image and video requires particularly large amount of bandwidth and storage space. Image compression technology is useful to overcome these problems. Various method of compression like transform coding, predictive coding, bit plane coding are available and are used efficiently. Compression systems based on hybrid coding which combines the advantages of different methods of image compression have also being developed over the last few years. Hybrid coding of images, deals with combining various approaches to enhance the individual methods and achieve better quality reconstructed images with higher compression ratio. In this paper, a hybrid approach to image compression is discussed. A compression method using neural-network and discrete wavelet transform is presented here. This scheme combines the high compression ratio of Self organizing map neural network with the good recreation property of discrete wavelet transform (DWT).The performance of proposed method is compared with the available SOFM NN based compression technique considering standard images.

TWO PHASE CLUSTERING METHOD FOR LEACH PROTOCOL FOR EFFECTIVE CLUSTER HEAD SELECTION

A Wireless Sensor Networks (WSN) comprise of a huge quantity of clusters of sensor nodes that can inter communicate with all nodes and with the base station through a wireless link. All clusters are corresponding to a cluster head to provide a direct communication to the base station and in addition with other cluster heads. Obviously well-organized selection of cluster head is a significant problem in the performance of the wireless sensor network. In this study, a novel two stage clustering protocol based on Self Organizing Map (SOM) neural network and Modified Fuzzy Possibilistic Clustering Algorithm (MFPCM) with the purpose of balancing the energy consumption. The clustering is carried out based on two important condition, energy level and coordinates of sensor nodes. Consequently, this novel two stage clustering method can prevent from premature death of the nodes and permit for random death of them. The experimental observation reveals that the SOM-MFPCM based cluster- head selection algorithm performs better than the cluster head election approach used in LEACH and can enable the network lifetime longer with less number of dead nodes.

Hierarchical Self Organizing Map for Novelty Detection using Mobile Robot with Robust Sensor

This paper presents a novelty detection method based on Self Organizing Map neural network using a mobile robot. Based on hierarchical neural network, the network is divided into three networks; position, orientation and sensor measurement network. A simulation was done to demonstrate and validate the proposed method using MobileSim. Three cases of abnormal events; new, missing and shifted objects are employed for performance evaluation. The result of detection was then filtered for false positive detection. The result shows that the inspection produced less than 2% false positive detection at high sensitivity settings.

A New Method For Clustering In Credit Scoring Problems

Due to the recent financial crisis and regulatory concerns of Basel II, credit risk assessment has become one of the most important topics in the financial risk management. Quantitative credit scoring models are widely used to assess credit risk in financial institutions. In this paper we introduce Time Adaptive self organizing Map Neural Network to cluster creditworthy customers against non credit worthy ones. We test this Neural Network on Australian credit data set and compare the results with other clustering Algorithm’s include K-means, PAM, SOM against different internal and external measures. TASOM has the best performance in clusters customers.

On the Use of a Cluster Ensemble Cloud Classification Technique in Satellite Precipitation Estimation

In this paper, the link-based cluster ensemble (LCE) method is utilized to improve cloud classification and satellite precipitation estimation. High resolution Satellite Precipitation Estimation (SPE) is based on the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Network Cloud Classification (PERSIANN-CCS) algorithm. This modified SPE with the incorporation of LCE involves the following four steps: 1) segmentation of infrared cloud images into patches; 2) cloud patch feature extraction; 3) clustering cloud patches using LCE; and 4) dynamic application of brightness temperature (Tb) and rain-rate relationships, derived using satellite observations. In order to cluster the cloud patches, the LCE method combines multiple data partitions from different clustering methods. The results show that using the cluster ensemble increases the performance of rainfall estimates compared to the SPE algorithm using a Self Organizing Map (SOM) neural network. The false alarm ratio (FAR), probabilities of detection (POD), equitable threat score (ETS), and bias are used as quantitative measures to assess the performance of the algorithm. It is shown that both the ETS and bias provide improvement in the summer and winter seasons. Almost 5% ETS improvement is obtained at some threshold values for the winter season using the cluster ensemble.

Utilization of a Self Organizing Map as a Tool to Study and Predict the Success of Engineering Students at Walailak University

Many factors have an influence on the success of undergraduate students particularly in engineering programs. Some students have to drop out as a result of obtaining very poor GPA (grade point average) and/or GPAX (accumulated grade point average) after only their first year of studying. It would be helpful for students if they know how their current GPA/GPAX could be improved in order to successfully graduate. In addition, what would be the expected outcome of their study, if their current GPAs of compulsory subjects are not fairly good? In this paper, the Self Organizing Map (SOM) neural network is utilized as a tool to cluster engineering student data into different groups by means of their study results. The results are then used to produce the weight maps. The maps reflect the correlation between GPA/GPAX of the compulsory subjects and the educational status of students. The result from the SOM with some adaptations to its matching phase is also used to create a predictor which is capable of producing a fairly high degree of correctness. The meaningful results are intended to be used as a guideline for students to prepare and improve themselves. In addition, it might be useful for student advisors and counselors to give appropriate advice to students whose GPAX are critically low. This can be accomplished by advising students to register less or withdraw some subjects in order to leverage their GPAX. In addition, some students should be advised to change their field of study if they perform fairly poorly in all compulsory subjects. The approach utilized in this paper is a novel one with respect to this application domain.