Hardware Trojan Detection Technique Based on SOM Neural Network

A fault detection and diagnosis method for the hydraulic servo system based on adaptive threshold and self-organizing map (SOM) neural network is proposed in this study. The nonlinear, time-varying, fluid-solid coupling properties of the hydraulic servo system are considered. Fault detection is realized based on a two-stage radial basis function (RBF) neural network model. The first-stage RBF neural network is adopted as a fault observer for the hydraulic servo system; the residual error signal is generated by comparing the estimated observer output with the actual measurements. To overcome the drawback of false alarms when the traditional fixed fault threshold is used, an adaptive threshold producer is established by the second-stage RBF neural network. Fault occurrence is detected by comparing the residual error signal with the adaptive threshold. When a system fault is detected, the SOM neural network is employed to implement fault classification and isolation by analyzing the features of the residual error signal. Three types of common faults are simulated to verify the performance and effectiveness of the proposed method. Experimental results demonstrate that the proposed method based on adaptive threshold and SOM neural network is effective in detecting and isolating the failure of the hydraulic servo system.

To improve the robustness of performance assessment for aileron actuators, a novel performance assessment for aileron actuators using multi-fractal detrended fluctuation analysis (MF-DFA) and self-organizing mapping (SOM) neural network is first proposed. First, a generalized regression neural network (GRNN) is employed to establish a fault observer, then the residue is generated by subtracting the actual actuator system output from the observer output. Second, the residue's feature is extracted by MF-DFA. Third, SOM neural network, trained only by the normal residue's feature, is used to obtain the minimum quantization error (MQE) between the current feature and the normal feature, and the health confidence value (CV) is obtained by normalizing MQE. Lastly, four types of faults, namely electronic amplifier gain abrupt change, electronic amplifier gain gradual change, sensor gain change and cylinder leakage, are applied to validate the effectiveness of the proposed method.

In this work, we propose a solution for the recognition of protein folds using Self-Organizing Map (SOM) neural network and present a comparison between few approaches. We use SOM, Fisher’s Linear Discriminant Analysis (FLD), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) methods for the recognition of three SCOP folds with six attributes (amino acid composition, predicted secondary structure, hydrophobicity, normalized van der Waals volume, polarity and polarizability). Then we classify the most common 27 SCOP folds using 125 dimensional data formed by the six attributes. This paper has a novelty in the way of applying SOM to these six attributes, and also portrays the capabilities of SOM among the other methods in protein fold classification. Firstly for the threeclass problem, the methods are tested on 120 proteins by applying 10-fold cross-validation technique and 93.33% classification performance is obtained with SOM. Secondly for the 27-class problem SOM is tested on 694 proteins by applying one-versus-others technique and 73.37% classification performance is obtained. Keywords: Protein fold recognition, protein fold classification, neural networks, self-organizing map.

The main goal of this paper is to show that the fractal analysis based on the continuous wavelet transform is not able to improve lithofacies classification using the self-organizing map (SOM) neural network model from well-logs data. The proposed idea consists to inject many inputs in SOM neural network machines and to choose the best map. These inputs are: data set 1: the five raw well-logs data which are: the gamma ray, density, neutron porosity, photoelectric absorption coefficient and sonic well-log; data set 2: the estimated Holder exponents using the continuous wavelet transform of the data set 1; data set 3: data set 1 and the three radioactive elements concentrations; data set 4: the estimated Holder exponents of the data set 1 and the Holder exponents of the radioactive elements concentrations; data set 5: the estimated Holder exponents of the data set 1 and the three radioactive elements concentrations logs. Application of the proposed idea at two boreholes located in the Algerian Sahara shows that the Holder exponents estimated with the continuous wavelet transform as an input of the SOM neural network are not able to give geological details. However, the raw well-logs as an input give more details and precision especially when they are enhanced with the natural gamma ray spectrometry data.

The use of neural networks in quality control has been a popular research topic over the last decade. An adaptive self-organizing mapping (SOM) neural network algorithm is proposed to overcome the shortages of traditional neural networks in this paper. In order to improve the classification effectiveness of SOM neural network, this paper designs an improved SOM neural network, which improved the algorithm formula based on input vector, the number setting of competitive layer neurons and the initializing weight vector. And the method is used to classify the product of cement slide shoe bearing in manufacturing process quality control, and experiment results show that the algorithm adapts well the unsupervised learning problems. Keywords-Self-organizing Mapping (SOM), artificial neural network, manufacturing process quality control

Self-Organizing Mapping (SOM) neural network has been widely used in pattern classification, vector quantization, and image compression. We consider the problem of strengthening the reliability of a SOM neural network by the technique of fault immunization of the synaptic links of each neuron which is similar to the concept of biological immunization. Instead of assuming the stuck-at-0 and stuck-at-1 as in those studies, we consider a general case of stuck-at-a, where a is a real value. The only assumption that we consider is only one neuron can be faulty at any time. No restriction on the number of faulty links of the neuron. Let wi,j be the weight of synaptic link j of neuron i obtained after the winner-take-all classification. Weight wi,j is immunized by adding a constant ∊i,j, either positive or negative, to wi,j. A neuron reaches its maximum fault immunization if the value of wi,j + ∊i,j can be either increased or decreased as much as possible without creating any misclassification. Thus, the fault immunization problem is formulated as an optimization problem on finding the value of each ∊i,j. A technique to find the value of wi,j + ∊i,j is proposed and its application to enhance the transmission reliability in image compression area is introduced.

The concept of software-defined satellite (SDS) enables in-orbit software reconfiguration and payload virtualization, significantly enhancing the configurability and cross-mission reusability of imaging payloads. However, this flexibility also exposes traditional star identification methods-which depend on fixed intrinsic camera parameters-to severe robustness limitations when dealing with multiple cameras and varying operational conditions. Therefore, a universal and adaptive star identification algorithm is essential to ensure stable and precise attitude determination within the SDS architecture. To this end, this paper proposes a parameter-independent star identification algorithm based on topological invariance features. The method integrates a topological invariant feature coordinate system with a self-organizing map (SOM) neural network, enabling consistent recognition across diverse payload configurations and observation scenarios. Extensive experiments under conditions such as stellar position noise, false-star noise, missing-star noise, focal length variation, and dynamic fields of view demonstrate that the proposed algorithm consistently outperforms three benchmark methods and maintains stable accuracy. These results confirm that the proposed method not only aligns with the SDS philosophy of hardware-software decoupling but also provides a necessary algorithmic foundation for its flexible, reconfigurable, and multi-payload attitude determination framework.

Music is an indispensable part of our life and study and is one of the most important forms of multimedia applications. With the development of deep learning and neural network in recent years, how to use cutting-edge technology to study and apply music has become a research hotspot. Music waveform is not only the main form of music frequency but also the basis of music feature extraction. This paper first designs a method of note extraction based on the fast Fourier transform principle of the audio signal packet route under the self-organizing map (SOM neural network) which can accurately extract the musical features of the note, such as amplitude, loudness, period, and so on. Secondly, the audio segments are divided into summary by adding window moving matching method, and the music features such as amplitude, loudness, and period of each bar are obtained according to the performance of audio signal in each bar. Finally, according to the similarity of the audio music theory of the adjacent summary of each bar, the audio segments are divided, and the music features of each segment are obtained. The traditional recurrent neural network (RNN) is improved, and the SOM neural network is used to recognize the audio emotion features. The final experimental results show that the proposed method based on SOM neural network and big data can effectively extract and analyze music waveform features. Compared with previous studies, this paper creatively proposed a new algorithm, which can more accurately and quickly extract and analyze the data sound waveform, and used SOM neural network to analyze the emotion model contained in music for the first time.

The Self-Organizing Neural Network Map (SOM) may be considered as good tools for classification and clustering process. Therefore, their performance is investigated in this work to make out the main flaw and propose the suitable improvements to overpass their limitations. These latter were extracted by the use of the SOM to classify a set of values with two different dimensions, the computational time cost and the input space normalization were the main deficiencies faced in this process. It's to note that the SOM was used without any kind of supervising or pre-training. The proposed improvement to overpass these deficiencies relies on enhancing the normalization mechanism with more intelligent techniques that can reduce the computational time and regularize the input space to avoid these limitations.

In view of present advancements in computing, with the development of distributed environment, many problems have to deal with distributed input data where individual data privacy is the most important issue to be addressed, for the concern of data owner by extending the privacy preserving notion to the original learning algorithms. Privacy Preserving Data Mining has become an active research area in addressing various privacy issues while bringing out solutions for them. There has been lot of progress in developing secure algorithms and models, able to preserve privacy using various data mining techniques like association, classification and clustering, where as importance of privacy preserving techniques applied for learning algorithms related to neural networks for mining problems are still in infancy. Our work in this paper focused on preserving privacy of an individual, using self organizing map (SOM) adopted for collaborative clustering of distributed data between multiple parties. We present Privacy Preserving Collaborative Clustering method using SOM (PPCSOM) for Horizontal Data Distribution, which allows multiple parties perform clustering in a collaborative approach using SOM neural network, without revealing their data directly to each other, in order to preserve privacy of all parties. Our simulation results shows that implementation of PPCSOM method achieves comparable accuracy and assured Privacy than the original non privacy preserving SOM algorithm.

The paper presents a readily implementable approach for sensor fault detection, identification (SFD/I) and faulted sensor data reconstruction in complex systems based on self-organizing map neural networks (SOMNNs). Two operational regimes are considered, i.e. the steady operation and operation with transients. For steady operation, SOMNN based estimation error (EE) are used for SFD. EE contribution plots are employed for SFI. For operation with transients, SOMNN classification maps are used for SFD/I comparing with the `fingerprint' maps. In addition, extension algorithm of SOMNNs is developed for faulted sensor data reconstruction. The validation of the proposed approach is demonstrated through experimental data during the commissioning of industrial gas turbines.

This paper describes the architecture design of novel massively parallel self-organizing map (SOM) neural networks. The proposed architecture, referred to as the planar SOM (PSOM), is described as a soft IP core synthesized in VHDL. The SOM neural network’s size and the input data vectors’ dimension are adjustable parameters. In this work, several SOM architectures are synthesized and their performance is evaluated for Xilinx Virtex-7 FPGAs. The presented hardware architecture allows online learning and can be easily adapted to a large variety of SOM topologies without a considerable design effort. A [Formula: see text] SOM hardware is validated through the FPGA implementation and its performances with an estimated working frequency of 297[Formula: see text]MHz for a 23-element input vector will reach 21,970 MCUPS in the learning phase and 35,902 MCPS in the recall one.

To detect and estimate direct sequence spread spectrum (DSSS) signals in a non-cooperative scene, an novel algorithm based on the self organizing map (SOM) neural network is proposed in this paper. We demonstrate by simulation that this method can indeed well perform the PN sequence detection and estimation under a low signal to noise ratio (SNR), and it is much superior to any other traditional methods. Keywords-PN code sequence; SOM neural network;DSSS I. INTRODUCTION

In recent decades, voltage instability has become an important contributor to large-scale power outages globally. Periodic voltage security assessments are key for the secure operation of power systems. There are various indicators which have been implemented in different types and complexity of systems to provide a measure of closeness to voltage collapse, but some of these algorithms may require advanced computational resources or may not perform well, depending on the prevailing conditions. Expanding interconnected power systems is needed like never before due to the increasing world demand for electricity, and voltage stability is therefore a worth consideration. The growing demand penetration of solar photovoltaic (PV) systems has some potential stability challenges due to their intermittent characteristics. Existing research evaluates the test systems of voltage stability and does not optimize using Intelligent models, which have variable power generation from Solar PV and Wind, as well as uncertainties in load demand and the initial step considering the evaluation of the model have to cover either inputs. A feed–forward back propagation artificial neural network (ANN) for voltage stability assessment to classify the operating scenario of a power system into one of two states, i.e. safe or unsafe. Neural network input data is generated from load flow analysis using Newton-Raphson (NR), and stability indices are computed over different scenarios. Line Stability Index (Lmn), counts the stability of a transmission line in power systems and provides an indication of its potential voltage collapse. The Fast Voltage Stability Index (FVSI), which also estimates line stability through reactive power and impedance characteristics. Voltage Collapse Proximity Index (VCPI) assesses system proximity to voltage collapse and identifies weak transmission lines. Values approaching 1 suggest voltage collapse conditions. The FVSI, VCPI, and Lmn are compared to a baseline NR stability index. Moreover, self-organizing map (SOM) neural network is used for stability results classification. Multi-objective grasshopper optimization algorithm (MOGOA) has been employed as an optimization network on the dual axis solar PV and wind turbine-based hybrid power generation system to analyze technical, economic, as well as environmental relations of energy conversion. The findings reveal that the adapted grid-tied solar-wind composite power system substantially mitigates the power deficiencies, reduces the real-power outages, diminishes the motion voltage small amendments, maximizes voltage prosperity, and improves power system resilience. The main findings show a significant reduction in real power loss, which went from 289,391.30 kW to only 5.43 kW, and a remarkable decrease in voltage deviation, which reduced from 0.56 p.u to 0.0493 p.u. Ensures Improved Service Quality and Enhanced Equipment Protection While gas generation costs increased by a small amount, it is important to note that this is an acceptable trade-off for a more robust and sustainable system, with costs per kWh of $0.142 vs $0.2881 per kWh, respectively. In addition, stability index was substantially decreased from 0.7666 to 0.0768, indicating better grid and resilience to disturbances. Findings emphasize the contribution of embedding MOGOA in conjunction with machine learning models including ANN and Self Organizing Maps (SOM) to fulfill the primal requisites of stability, efficiency and sustainability in modernized power systems, illustrating a coupled advancement in the realm of power systems engineering and optimization.

SummaryIn order to suppress the interference in local area networks, this paper presents a Wireless Local Area Networks (WLAN) interference self‐optimization method based on a Self‐Organizing Feature Map (SOM) neural network model. This method trains the model by using original data sets as the initial vector set and using the whole Signal to Interference plus Noise Ratio (SINR) vector generated by the change of one Wireless Access Point (AP) channel as the basic feature. After the training, the SOM neural network can quickly locate the fault AP and optimize the network according to the changes of the network environment. Simulation results reveal that the proposed scheme can efficiently locate the AP where interference happens and optimize the interference with an improved user experience. Copyright © 2016 John Wiley & Sons, Ltd.