C-Support Vector Classification Research Articles

Geographical origin identification of Khao Dawk Mali 105 rice using combination of FT-NIR spectroscopy and machine learning algorithms

The mislabelled Khao Dawk Mali 105 rice coming from other geographical region outside the Thung Kula Rong Hai region is extremely profitable and difficult to detect; to prevent retail fraud (that adversely affects both the food industry and consumers), it is vital to identify geographical origin. Near infrared spectroscopy can be used to detect the specific content of organic moieties in agricultural and food products. The present study implemented the combinatorial method of FT-NIR spectroscopy with chemometrics to identify geographical origin of Khao Dawk Mali 105 rice. Rice samples were collected from 2 different region including the north and northeast of Thailand. NIR spectra data were collected in range of 12,500 – 4,000 cm−1 (800–2,500 nm). Five machine learning algorithms including linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), C-support vector classification (C-SVC), backpropagation neural networks (BPNN), hybrid principal component analysis-neural network (PC-NN) and K-nearest neighbors (KNN) were employed to classify NIR data of rice samples with full wavelength and selected wavelength by Extremely Randomized Trees (Extra trees) algorithm. Based on the findings, geographical origin of rice could be specified quickly, cheaply, and reliably using combination of NIRS and machine learning. All models creating by full wavelength and selected wavelength exhibited accuracy between 65 and 100 % for identifying geographical region of rice. It was proven that NIR spectroscopy may be used for the quick and non-destructive identification of geographical origin of Khao Dawk Mali 105 rice.

Fall prediction in a quiet standing balance test via machine learning: Is it possible?

The elderly population is growing rapidly in the world and falls are becoming a big problem for society. Currently, clinical assessments of gait and posture include functional evaluations, objective, and subjective scales. They are considered the gold standard to indicate optimal mobility and performance individually, but their sensitivity and specificity are not good enough to predict who is at higher risk of falling. An innovative approach for fall prediction is the machine learning. Machine learning is a computer-science area that uses statistics and optimization methods in a large amount of data to make outcome predictions. Thus, to assess the performance of machine learning algorithms in classify participants by age, number of falls and falls frequency based on features extracted from a public database of stabilometric assessments. 163 participants (116 women and 47 men) between 18 and 85 years old, 44.0 to 75.9 kg mass, 140.0 to 189.8 cm tall, and 17.2 to 31.9 kg/m2 body mass index. Six different machine learning algorithms were tested for this classification, which included Logistic Regression, Linear Discriminant Analysis, K Nearest-neighbours, Decision Tree Classifier, Gaussian Naive Bayes and C-Support Vector Classification. The machine learning algorithms were applied in this database which has sociocultural, demographic, and health status information about participants. All algorithm models were able to classify the participants into young or old, but our main goal was not achieved, no model identified participants at high risk of falling. Our conclusion corroborates other works in the biomechanics field, arguing the static posturography, probably due to the low daily living activities specificity, does not have the desired effects in predicting the risk of falling. Further studies should focus on dynamic posturography to assess the risk of falls.

Is Someone There or Is That the TV? Detecting Social Presence Using Sound

Social robots in the home will need to solve audio identification problems to better interact with their users. This article focuses on the classification between (a) natural conversation that includes at least one co-located user and (b) media that is playing from electronic sources and does not require a social response, such as television shows. This classification can help social robots detect a user’s social presence using sound. Social robots that are able to solve this problem can apply this information to assist them in making decisions, such as determining when and how to appropriately engage human users. We compiled a dataset from a variety of acoustic environments that contained either natural or media audio, including audio that we recorded in our own homes. Using this dataset, we performed an experimental evaluation on a range of traditional machine learning classifiers and assessed the classifiers’ abilities to generalize to new recordings, acoustic conditions, and environments. We conclude that a C-Support Vector Classification (SVC) algorithm outperformed other classifiers. Finally, we present a classification pipeline that in-home robots can utilize, and we discuss the timing and size of the trained classifiers as well as privacy and ethics considerations.

Abstract LB070: Predicting distant recurrences in invasive breast carcinoma patients using their clinicopathological profiles

Abstract Purpose: To determine whether the classification-based machine learning (ML) or artificially intelligent (AI) techniques can predict distant recurrence flags (yes or no) in invasive breast cancer patients using the data comprising several clinicopathological measurements such as pathological staging of tumor and surrounding nodes deemed both pre and post neoadjuvant (i.e. either chemo, radiation or hormone based) therapy including imaging based therapy-response as well and the status of adjuvant therapy (Chemo or Anti-Her2/Neu antibody) therapy were administered to patients post resection of their tumor to minimize the possibility of diseases-recurrence in them. Method: The clinicopathological data from a retrospective study of 900 breast cancer patients at Duke university (posted at The Cancer Imaging Archive, i.e. TCIA) was tapped for our study. These patients received neoadjuvant therapies such as Chemo-, Radiation or Endocrine Hormone based therapy, had their responses evaluated through imaging and pathological staging (both tumor and nodal), and received Adjuvant therapies (such as Chemo or Anti-Her2/Neu antibody therapy) post tumor resection. The patient entries pertaining to ungraded tumor responses from therapies or those labelled as ‘Not applicable (NA)’ for any of the aforementioned clinicopathological parameters were filtered out retaining 161 patient-entries which were further split into train and test sets in 90:10 ratio where 90% were used for training (ntrain=144) and 10% for testing (ntest=17) of several ML models. Classification based machine learning models such as Random Forest (RF), C-Support Vector Classification (SVC) and Supervised Neural Network (aka Multi-Layer Perceptron, i.e. MLP) were employed to train and test aforementioned clinicopathological parameters to predict distant recurrence flags or labels (i.e. Yes or No). The training of the model was conducted using ImaGene software. Out of the three aforementioned models, RF seemed to have performed best with grid search activated over tree-depth hyperparameter to predict distant recurrence flags in test patients (ntest=17) at AUC=1.0 (p<0.002). Further, the validation of the model was conducted using external clinicopathological dataset of 20 patients from Dartmouth-Hitchcock (DH) Medical center which yielded AUC>0.75. Conclusion: Random Forest model trained using grid search through tree-depth hyperparameter can predict distant recurrence flags (Yes or No) in invasive breast cancer patients at AUC in the range of 0.75 1.0 across multiple institutions, thereby aiding portability of models and advancing the idea of multi-instutional ML/AI model validations in Invasive Breast Carcinoma subtype of Breast Cancer. Citation Format: Shrey S. Sukhadia, Kristen Muller, Adrienne Workman, Shivashankar Nagaraj, Olivier Gevaert. Predicting distant recurrences in invasive breast carcinoma patients using their clinicopathological profiles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB070.

Highly Accurate Spam Detection with the Help of Feature Selection and Data Transformation

The amount of spam is increasing rapidly while the popularity of emails is increasing. This situation has led to the need to filter spam emails. To date, many knowledge-based, learning-based, and clustering-based methods have been developed for filtering spam emails. In this study, machine-learning-based spam detection was targeted, and C4.5, ID3, RndTree, C-Support Vector Classification (C-SVC), and Naïve Bayes algorithms were used for email spam detection. In addition, feature selection and data transformation methods were used to increase spam detection success. Experiments were performed on the UC Irvine Machine Learning Repository (UCI) spambase dataset, and the results were compared for accuracy, Receiver Operating Characteristic (ROC) analysis, and classification speed. According to the accuracy comparison, the C-SVC algorithm gave the highest accuracy with 93.13%, followed by the RndTree algorithm. According to the ROC analysis, the RndTree algorithm gave the best Area Under Curve (AUC) value of 0.999, while the C4.5 algorithm gave the second-best result. The most successful methods in terms of classification speed are Naïve Bayes and RndTree algorithms. In the experiments, it was seen that feature selection and data transformation methods increased spam detection success. The binary transformation that increased the classification success the most and the feature selection method was forward selection.

Mid-Infrared Imaging Characterization to Differentiate Lung Cancer Subtypes.

Introduction: Lung cancer is the most common malignancy worldwide. Squamous cell carcinoma (SQ) and adenocarcinoma (LUAD) are the two most frequent histological subtypes. Small cell carcinoma (SCLC) subtype has the worst prognosis. Differential diagnosis is essential for proper oncological treatment. Life science associated mid- and near-infrared based microscopic techniques have been developed exponentially, especially in the past decade. Vibrational spectroscopy is a potential non-destructive approach to investigate malignancies. Aims: Our goal was to differentiate lung cancer subtypes by their label-free mid-infrared spectra using supervised multivariate analyses. Material and Methods: Formalin-fixed paraffin-embedded (FFPE) samples were selected from the archives. Three subtypes were selected for each group: 10-10 cases SQ, LUAD and SCLC. 2 μm thick sections were cut and laid on aluminium coated glass slides. Transflection optical setup was applied on Perkin-Elmer infrared microscope. 250 × 600 μm areas were imaged and the so-called mid-infrared fingerprint region (1800-648cm−1) was further analysed with linear discriminant analysis (LDA) and support vector machine (SVM) methods. Results: Both “patient-based” and “pixel-based” approaches were examined. Patient-based analysis by using 3 LDA models and 2 SVM models resulted in different separations. The higher the cut-off value the lower is the accuracy. The linear C-support vector classification (C-SVC) SVM resulted in the best (100%) accuracy for the three subtypes using a 50% cut-off value. The pixel-based analysis gave, similarly, the linear C-SVC SVM model to be the most efficient in the statistical indicators (SQ sensitivity 81.65%, LUAD sensitivity 82.89% and SCLC sensitivity 88.89%). The spectra cut-off, the kernel function and the algorithm function influence the accuracy. Conclusion: Mid-Infrared imaging could be used to differentiate FFPE lung cancer subtypes. Supervised multivariate tools are promising to accurately separate lung tumor subtypes. The long-term perspective is to develop a spectroscopy-based diagnostic tool, revolutionizing medical differential diagnostics, especially cancer identification.

Low-cost spectroscopic devices with multivariate analysis applied to milk authenticity

Milk is one of the main foods present in the human diet. It is a good source of many essential nutrients; however, milk is among the most adulterated food globally. This study investigated the potential of low-cost portable spectroscopy near-infrared (NIR) and energy-dispersive X-ray fluorescence (EDXRF) spectrometry combined with chemometric tools to discriminate authentic cow and goat milk from adulterated ones with whey. The common dimension (ComDim) multi-block analysis enabled us to verify that the EDXRF data had good discrimination between the samples. In contrast, the NIR spectra had no evident discrimination and showed a nonlinear behavior in their data. Both devices had equivalent classification performances of 98.4 to 100% accuracy for the prediction when combined with the appropriate algorithms, such as partial least squares discriminant analysis (PLS-DA) for EDXRF and C-support vector classification (C-SVC) for NIR. Discriminant methods have some limitations for authentication tasks, so class modeling methods such as data driven soft independent modeling of class analogy (DD-SIMCA) were more helpful for this problem, with a prediction accuracy greater than 98.9% for both devices. These results suggest that NIR and EDXRF can be reliably applied to verify milk authenticity. Besides, these devices are portable, cost-effective, user-friendly, fast, robust, do not require extensive sample preparations, and can be easily adapted to desired settings.

How can machine learning identify suicidal ideation from user's texts? Towards the explanation of the Boamente system

Suicidal Ideation (SI) is characterized by a desire to die and, in many cases, even planning a suicide with writing notes or farewell letters. As a digital phenotyping-based application, the Boamente tool remotely detects patterns that indicate SI by passively collecting texts typed by patients under supervision of a mental health professional. The system uses Artificial Intelligence (AI) techniques in the process of classifying texts as positive or negative for suicidal ideation. However, there is no explanation yet on how texts influence the outputs of Machine/Deep Learning (ML/DL) models. This study aims to start the process of explaining how words and sentences of the collected texts influence the outputs of the classification models. The following classical ML algorithms were trained and models explained in this study: C-Support Vector Classification (SVC), Extra Trees, and Random Forest machine learning. The ELI5 method was used for the local and global interpretability of the algorithms. Features of each class were analyzed with their respective weight values on the output of each algorithm in the global interpretability. Classifications of three samples from the dataset were explained during the local interpretability step. After analyzing the features of each class, we were able to identify that the Extra Trees and Random Forest classifiers were equally influenced by features composed of one term or two terms. SVC was more influenced by features composed of two terms than just one term. In general, features including the features “suicide”, “desire to kill oneself” and “sadness” had a higher importance value to indicate positive for SI.

Digitized spiral drawing classification for Parkinson's disease diagnosis

Parkinson's disease (PD) is the most common neurodegenerative disease affecting significantly motor functions of elderly persons. The diagnosis and monitoring of PD is costly and inconvenient process even today, in under developing parts of the world. The observable symptoms of PD at early stage include disorders in handwriting and repetitive tasks of spiral drawing. With advancement of IT it is easier to collect spiral drawing samples using digitized tablet. We proposed detailed analysis of Static and dynamic spirals drawn by PD patients. For this, in-air and on-surface kinematic variables are taken out from data files generated for 25 patients and 15 healthy controls, using mathematical models. Results demonstrated nearly 91% classification accuracy to separate PD patients from healthy controls by applying feature engineering and four machine learning (ML) classifiers Logistic Regression, C-Support Vector Classification (SVC), K- nearest neighbor(KNN) classifier and ensemble model Random Forest Classifier(RFC). This paper confirms that digitized spiral drawings have major impact on classification of PD patients and healthy controls and hence can support future differential diagnosis of PD.

A Comparison of Various Algorithms for Classification of Food Scents Measured with an Ion Mobility Spectrometry.

The present aim was to compare the accuracy of several algorithms in classifying data collected from food scent samples. Measurements using an electronic nose (eNose) can be used for classification of different scents. An eNose was used to measure scent samples from seven food scent sources, both from an open plate and a sealed jar. The k-Nearest Neighbour (k-NN) classifier provides reasonable accuracy under certain conditions and uses traditionally the Euclidean distance for measuring the similarity of samples. Therefore, it was used as a baseline distance metric for the k-NN in this paper. Its classification accuracy was compared with the accuracies of the k-NN with 66 alternative distance metrics. In addition, 18 other classifiers were tested with raw eNose data. For each classifier various parameter settings were tried and compared. Overall, 304 different classifier variations were tested, which differed from each other in at least one parameter value. The results showed that Quadratic Discriminant Analysis, MLPClassifier, C-Support Vector Classification (SVC), and several different single hidden layer Neural Networks yielded lower misclassification rates applied to the raw data than k-NN with Euclidean distance. Both MLP Classifiers and SVC yielded misclassification rates of less than when applied to raw data. Furthermore, when applied both to the raw data and the data preprocessed by principal component analysis that explained at least or of the total variance in the raw data, Quadratic Discriminant Analysis outperformed the other classifiers. The findings of this study can be used for further algorithm development. They can also be used, for example, to improve the estimation of storage times of fruit.

SADM-SDNC: security anomaly detection and mitigation in software-defined networking using C-support vector classification

The inherent features of software-defined networking (SDN) architecture revolutionize traditional network infrastructure and provide the opportunity for integrated and centralized network monitoring. One of the shortcomings of SDNs is related to its high vulnerability to distributed denial of service attacks and other similar ones. In this paper, a novel multi-stage modular approach is proposed for detecting and mitigating security anomalies in SDN environment (SADM-SDNC). The proposed approach uses NetFlow protocol for gathering information and generating dataset and information gain ratio in order to select the effective features. Also, the C-support vector classification algorithm with radial basis function kernel, and features of Floodlight controller for developing a structure with desirable performance were used in the proposed scheme. The experimental results demonstrate that the proposed approach performs better than other methods in terms of enhancing accuracy and detection rate, and reducing classification error and false alarm rate, which were measured as 99.67%, 99.26%, 0.33%, and 0.08% respectively. Finally, thanks to utilizing REST API and Static Entry Pusher technologies in the Floodlight controller, it makes it possible to disconnect any communications with the attacking factors and remove destructive users.

Outcome of Severe Vaso-Occlusive Crisis in Sickle Cell Disease Adults Admitted to Referral Centers in Africa and Europe. Introduction of Machine Learning Methods to Improve the Presev Score

Introduction: Sickle Cell Disease (SCD) is one of the most common genetic disease worldwide. The Acute Chest Syndrome (ACS) is a leading cause of death for SCD patients. The PRESEV1 study was set to produce a predictive score to assess the risk of an ACS development (Bartolucci et al., 2016). PRESEV2 was an international, multicenter prospective confirmatory study to validate the PRESEV score. This study aims at improving these predictions with the addition of a machine learning (ML) method. Patients and methods: Included patients follow PRESEV1 and PRESEV2 studies 'rules. The dataset thus contains 97 patients who developed an ACS episode (18.3%) against 434 patients who did not (81.7%). To compute the PRESEV score, we firstly used the method developed previously with the following variables as input: leukocytes, reticulocytes, hemoglobin levels and cervical spine pain. This method is based on a decision tree with fixed rules and is referred to as the decision tree method throughout this abstract. Secondly we used a ML method using a combined sampling method named SMOTEENN to balance the data and a C-Support Vector Classification (SVC) with fixed parameters to predict the score. This method produces a probability, with a threshold of 0.2, under which the patient is predicted to declare an ACS. We considered the dataset composed of PRESEV1 dataset and 80 percent of PRESEV2 with a randomly choice. The test dataset is thus composed of the remaining 20 percent of PRESEV2. This technique of random choice allowed us to use a 50-cross-validation and compute with Python an average score and a standard deviation (std). In order to allow comparison of the developed score with or without the addition of the ML method, rates were calculated by adding the weight of ACS representation in the dataset. Results: Among all parameters analyzed, the SVC method considered the following variables for calculation of the score: leukocytes, LDH, urea, reticulocytes and hemoglobin levels. A hundred and two adult patients with a severe VOC requiring hospitalization were included. Out of this pool of patients, 26 (25.5%) were predicted with a low risk of developing an ACS episode (SVC method). Sensibility and specificity were of 94.7% and 26.8%, respectfully. The negative predictive value (NPV) was of 95.8% and the positive predictive value (PPV) of 22.4%. Results are resumed in table 1. When compared to the PRESEV score (decision tree method), 44 patients out of 372 were identified with a low risk score (11.8%), Discussion and Conclusion: While the addition of a ML method did not allow the improvement of the sensibility or the NPV of the PRESEV score, it improved both the specificity and the PPV. The addition of artificial intelligence thus provides a better prediction with a higher percentage of "low-risk" patients. As highlighted in the international PRESEV study, this score could represent a useful tool for physicians in hospital settings, with limited beds. While the PRESEV score could allow a better management of "low risk" patients on one side, the identification of "high-risk" patients could also represent a serious advantage to physicians, as it could improve the feasibility of clinical trials for the prevention of this lethal complication in SCD patients. Disclosures Bartolucci: Innovhem: Other; Novartis: Research Funding; Roche: Consultancy; Bluebird: Consultancy; Emmaus: Consultancy; Bluebird: Research Funding; Addmedica: Research Funding; AGIOS: Consultancy; Fabre Foundation: Research Funding; Novartis: Consultancy; ADDMEDICA: Consultancy; HEMANEXT: Consultancy; GBT: Consultancy.

DETERMINING THE STYLISTIC AFFILIATION OF THE TEXT USING ARTIFICIAL NEURAL NETWORKS

The research is about the problem of developing an effective way to determine the stylistic affiliation of texts. Styles such as scientific, journalistic and official-business are considered. Texts of one subject – about language – selected for the analysis. Different combinations of vectorization methods and architectures of artificial neural networks considered which would provide a high level of recognition. Among the architectures of artificial neural networks– Support Vector Machines (SVM) (C-Support Vector Classification (SVC), Epsilon-Support Vector Regression (SVR)) and Multi Layer Perseptron (MLP). Among the vectorization methods– HeshingVectorizer, CountVectorizer and TfidVectorizer. Studies have shown that all the approaches considered most effectively distinguish between official and business texts, due to their greatest standardization. Scientific and official business styles are especially effective. The considered methods show the least accuracy in determining the stylistic affiliation, when one of the styles is journalistic. The most effective approach to determining stylistic affiliation was the combination of the vectorization method and both architectures of artificial neural networks Support Vector Machines. In the previous stage, word stemming was increasing efficiency. In texts with at least 500 characters, this approach has ensured an accuracy of 94-98%, and the time for learning an artificial neural network does not exceed one second on computers of the standard configuration at this time. With the help of the Lime library, a visualization of the study of the operation of an artificial neural network presents, which is extremely important empirical material for philologists for further linguistic analysis.

A tool for the real-time evaluation of ECG signal quality and activity: Application to submaximal treadmill test in horses

This work describes a novel signal quality index (SQI), i.e. higher-order-statistics-SQI (hosSQI), for the real-time evaluation of electrocardiogram (ECG) recording quality. The hosSQI formula combines two already known SQIs, kurtosis (kSQI) and skewness (sSQI), exploiting the related properties to improve their performance.We validated hosSQI using 1000 human pre-labelled twelve-lead ECGs and compared its performance with the state-of-the-art indexes in the literature. Our index outperformed four existing indexes (kSQI, sSQI, basSQI, iorSQI), reaching an accuracy up to 90.38% in the signal quality discrimination. Afterwards, we employed these indexes to compare signal quality of ECGs acquired by two different monitoring systems (red-dot and textile electrode based), in unfavourable conditions in terms of motion artifacts, adhesion and mechanical firmness of electrodes. The existing four SQIs and hosSQI were updated each second, using equine ECGs recorded during submaximal treadmill test. Wilcoxon nonparametric statistical test showed that all the SQIs were significantly higher for textile than for red-dot electrodes. A pattern recognition algorithm was implemented to test a real-time discrimination of three activity conditions (walk, trot, and gallop) based on the SQIs. Given that hosSQI values computed for red-dot were under the acceptability threshold in more than 63% of signals, we used only the textile data. We employed a C-Support Vector Classification and we found the highest accuracy value in the discrimination of walk and gallop (84.91%). Even if these results are preliminary, we proposed a promising tool for the real-time assessment of ECG signal quality and physical activity, also during intense exercise.

Hybrid Kernel Based Machine Learning Using Received Signal Strength Measurements for Indoor Localization

In this paper, a new indoor localization algorithm with received signal strength indicators (RSSI) fingerprints by a kernel-based learning technique is proposed. In the offline phase, after the training set pre-processing by the iterative self-organizing data analysis techniques algorithm (ISODATA), the measurement-label set is utilized for classification learning by the c-support vector classification approach. Moreover, each measurement-position training subset is chosen for regressing learning with hybrid kernel and cross-validation techniques. In the online phase, based on the classification result of the received RSSI measurement, the corresponding regression function is chosen for target position estimation. Compared with existing learning techniques, it can increase the training ability of a localization system dramatically. The field tests show that, without adding any extra computation complexity, the proposed algorithm can obtain more accurate position estimation than other existing learning-based localization approaches achieve.

Improving prediction of heterodimeric protein complexes using combination with pairwise kernel

BackgroundSince many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers.ResultsIn this paper, we use three promising kernel functions, Min kernel and two pairwise kernels, which are Metric Learning Pairwise Kernel (MLPK) and Tensor Product Pairwise Kernel (TPPK). We also consider the normalization forms of Min kernel. Then, we combine Min kernel or its normalization form and one of the pairwise kernels by plugging. We applied kernels based on PPI, domain, phylogenetic profile, and subcellular localization properties to predicting heterodimers. Then, we evaluate our method by employing C-Support Vector Classification (C-SVC), carrying out 10-fold cross-validation, and calculating the average F-measures. The results suggest that the combination of normalized-Min-kernel and MLPK leads to the best F-measure and improved the performance of our previous work, which had been the best existing method so far.ConclusionsWe propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art.

Fault Diagnosis of a Reconfigurable Crawling–Rolling Robot Based on Support Vector Machines

As robots begin to perform jobs autonomously, with minimal or no human intervention, a new challenge arises: robots also need to autonomously detect errors and recover from faults. In this paper, we present a Support Vector Machine (SVM)-based fault diagnosis system for a bio-inspired reconfigurable robot named Scorpio. The diagnosis system needs to detect and classify faults while Scorpio uses its crawling and rolling locomotion modes. Specifically, we classify between faulty and non-faulty conditions by analyzing onboard Inertial Measurement Unit (IMU) sensor data. The data capture nine different locomotion gaits, which include rolling and crawling modes, at three different speeds. Statistical methods are applied to extract features and to reduce the dimensionality of original IMU sensor data features. These statistical features were given as inputs for training and testing. Additionally, the c-Support Vector Classification (c-SVC) and nu-SVC models of SVM, and their fault classification accuracies, were compared. The results show that the proposed SVM approach can be used to autonomously diagnose locomotion gait faults while the reconfigurable robot is in operation.

Modelling personal thermal sensations using C-Support Vector Classification (C-SVC) algorithm

The personalised conditioning system (PCS) is widely studied. Potentially, it is able to reduce energy consumption while securing occupants' thermal comfort requirements. It has been suggested that automatic optimised operation schemes for PCS should be introduced to avoid energy wastage and discomfort caused by inappropriate operation. In certain automatic operation schemes, personalised thermal sensation models are applied as key components to help in setting targets for PCS operation. In this research, a novel personal thermal sensation modelling method based on the C-Support Vector Classification (C-SVC) algorithm has been developed for PCS control. The personal thermal sensation modelling has been regarded as a classification problem. During the modelling process, the method ‘learns’ an occupant's thermal preferences from his/her feedback, environmental parameters and personal physiological and behavioural factors. The modelling method has been verified by comparing the actual thermal sensation vote (TSV) with the modelled one based on 20 individual cases. Furthermore, the accuracy of each individual thermal sensation model has been compared with the outcomes of the PMV model. The results indicate that the modelling method presented in this paper is an effective tool to model personal thermal sensations and could be integrated within the PCS for optimised system operation and control.

Damage identification system of CFRP using fiber Bragg grating sensors

Carbon fiber reinforced plastics (CFRP) structure is very sensitive to impact damage, thus an early identification of structural damage is necessary to avoid major structural failures. In this paper, structural damage identification system by using fiber Bragg grating (FBG) sensors and damage identification algorithm for CFRP structure were investigated. Structural dynamic response signals, which contain the damage information, were generated by an active actuation method, and detected by FBG sensors. Damage characteristics were extracted by the Fourier transform and principal component analysis (PCA) methods. Then, based on the damage characteristic, one-class support vector machines (SVM) method was used to detect the damage, and multi-class C-support vector classification (C-SVC) methods were used to identify the damage area and evaluate the damage degree. At last, the damage identification system was established and verified on a CFRP plate. Within a 160mm∗160mm∗2mm experiment area, the identification accuracy was more than 90%. This paper provided a reliable method for the CFRP structural damage identification.

Comparison of Different Optimization Methods with Support Vecto Machine for Blast Furnace Multi-Fault Classification

Aiming at the complex and volatile conditions of the blast furnace, a support vector machine with multi-fault classification is proposed to solve the problem on blast furnace fault diagnosis. The normal or failure data is handled with a normalization and dimensionality reduction of principal component analysis (PCA). As a small amount of failure sample, a C-support vector classification (C-SVC) is applied in this filed. Different optimization methods have been performed for optimizing SVM parameters, including the grid-search method (GSM), the genetic algorithm (GA) and the particle swarm optimization (PSO). It compares these optimization algorithm strengths and limitations for multi-fault classification on classification ability and classification speed.