Application of fuzzy c-means clustering for analysis of chemical ionization mass spectra: insights into the gas-phase chemistry of NO3-initiated oxidation of isoprene

Oxidation of volatile organic compounds (VOCs) can lead to the formation of secondary organic aerosol, a significant component of atmospheric fine particles, which can affect air quality, human health, and climate change. However, current understanding of the formation mechanism of SOA is still incomplete, which is not only due to the complexity of the chemistry, but also relates to analytical challenges in SOA precursor detection and quantification. Recent instrumental advances, especially the developments of high-resolution time-of-flight chemical ionization mass spectrometry (CIMS), greatly enhanced the capability to detect low- and extremely low-volatility organic molecules (L/ELVOCs). Although detection and characterization of low volatility vapors largely improved our understanding of SOA formation, analyzing and interpreting complex mass spectrometric data remains a challenging task. This necessitates the use of dimension-reduction techniques to simplify mass spectrometric data with the purpose of extracting chemical and kinetic information of the investigated system. Here we present an approach by using fuzzy c-means clustering (FCM) to analyze CIMS data from chamber experiments aiming to investigate the gas-phase chemistry of nitrate radical initiated oxidation of isoprene. The performance of FCM was evaluated and validated. By applying FCM various oxidation products were classified into different groups according to their chemical and kinetic properties, and the common patterns of their time series were identified, which gave insights into the chemistry of the system investigated. The chemical properties are characterized by elemental ratios and average carbon oxidation state, and the kinetic behaviors are parameterized with generation number and effective rate coefficient (describing the average reactivity of a species) by using the gamma kinetic parameterization model. In addition, the fuzziness of FCM algorithm provides a possibility to separate isomers or different chemical processes species are involved in, which could be useful for mechanism development. Overall FCM is a well applicable technique to simplify complex mass spectrometric data, and the chemical and kinetic properties derived from clustering can be utilized to understand the reaction system of interest.</p

Computational Modeling Approaches For Task Analysis In Robotic-Assisted Surgery

Surgery is continuously subject to technological innovations including the introduction of robotic surgical devices. The ultimate goal is to program the surgical robot to perform certain difficult or complex surgical tasks in an autonomous manner. The feasibility of current robotic surgery systems to record quantitative motion and video data motivates developing descriptive mathematical models to recognize, classify and analyze surgical tasks. Recent advances in machine learning research for uncovering concealed patterns in huge data sets, like kinematic and video data, offer a possibility to better understand surgical procedures from a system point of view. This dissertation focuses on bridging the gap between these two lines of the research by developing computational models for task analysis in robotic-assisted surgery. The key step for advance study in robotic-assisted surgery and autonomous skill assessment is to develop techniques that are capable of recognizing fundamental surgical tasks intelligently. Surgical tasks and at a more granular level, surgical gestures, need to be quantified to make them amenable for further study. To answer to this query, we introduce a new framework, namely DTW-kNN, to recognize and classify three important surgical tasks including suturing, needle passing and knot tying based on kinematic data captured using da Vinci robotic surgery system. Our proposed method needs minimum preprocessing that results in simple, straightforward and accurate framework which can be applied for any autonomous control system. We also propose an unsupervised gesture segmentation and recognition (UGSR) method which has the ability to automatically segment and recognize temporal sequence of gestures in RMIS task. We also extent our model by applying soft boundary segmentation (Soft-UGSR) to address some of the challenges that exist in the surgical motion segmentation. The proposed algorithm can effectively model gradual transitions between surgical activities. Additionally, surgical training is undergoing a paradigm shift with more emphasis on the development of technical skills earlier in training. Thus metrics for the skills, especially objective metrics, become crucial. One field of surgery where such techniques can be developed is robotic surgery, as here all movements are already digitalized and therefore easily susceptible to analysis. Robotic surgery requires surgeons to perform a much longer and difficult training process which create numerous new challenges for surgical training. Hence, a new method of surgical skill assessment is required to ensure that surgeons have adequate skill level to be allowed to operate freely on patients. Among many possible approaches, those that provide noninvasive monitoring of expert surgeon and have the ability to automatically evaluate surgeon\u27s skill are of increased interest. Therefore, in this dissertation we develop a predictive framework for surgical skill assessment to automatically evaluate performance of surgeon in RMIS. Our classification framework is based on the Global Movement Features (GMFs) which extracted from kinematic movement data. The proposed method addresses some of the limitations in previous work and gives more insight about underlying patterns of surgical skill levels

Klastering Emosi Berdasarkan Gelombang Otak Sinyal EEG Menggunakan Fuzzy C-Means Clustering

Berdasarkan ilmu psikologi, emosi berpengaruh besar terhadap kualitas dan kuantitas dari aktivitas individu. Keadaan emosi individu dapat dilihat secara nyata melalui ekpresi wajah maupun nada bicara. Selain melalui fitur wajah maupun fitur suara, identifikasi emosi juga bisa dilakukan melalui gelombang otak. Pada tugas akhir ini penulis menggunakan sinyal electroencephalogram sebagai input untuk melakukan klastering emosi. Sinyal electroencephalogram ini dipilih karena dapat merekam emosi sebenarnya dari individu. Dengan menggunakan pengukuran statistik pada domain waktu sinyal, fitur-fitur yang terdapat dalam sinyal EEG dijadikan acuan untuk melakukan klastering emosi. Fitur didapatkan dari delapan channel yaitu channel F8, T7, CP1, CP2, P7, FC2, F4 dan AF3. Emosi yang diolah dan dianalisis yaitu senang, sedih, puas, terkejut, terlindung, tidak peduli, marah dan takut berdasakan parameter valence, arousal dan dominance. Klastering sinyal dilakukan dengan menggunakan Fuzzy C-Means Clustering. Banyaknya cluster menunjukkan banyaknya emosi yang akan dikenali. Penelitian ini menghasilkan nilai output berupa sistem yang dapat mengelompokkan emosi. Nilai akurasi tertinggi didapatkan pada kondisi C=2 pada kombinasi channel F8, T7, CP1, CP2, P7, FC2 dan F4 dengan nilai akurasi rand index 60.31%. ================================================================================================== In psychology, emotions greatly a_ect the quality and quantity of individual activities. The emotional state of an individu can be seen through the real expression of the face and tone of speech. Other than using facial features as well as voice features, emotional recognition can also be done based on brain wave signal. In this final project author use electroencephalogram signal as input to do cluster some of human emotion. This electroencephalogram signal is chosen because it can record the actual emotions of the individu. Using statistical measurements in time domain signal, the features contained in EEG signals were used to cluster emotions. Features obtained from eight channels which are F8, T7, CP1, CP2, P7, FC2, F4 and AF3. Emotions that were processed and analyzed are happy, sad, satisfied, surprised, protected, unconcerned, angry and frightened based on parameters valence, arousal and dominance. The signal clustering was performed using Fuzzy C-Means Clustering. The number of clusters shows the number of emotions to be recognized. This research produces an output value of a system that can clustering emotions. The highest accuracy is obtained when C=2 in combination of channel F8, T7, CP1, CP2, P7, FC2 and F4 with 60.31% of rand index accuration valu

Técnicas de aprendizaje automático en estudios epidemiológicos longitudinales. Aplicación a la Cohorte EpiChron de investigación en multimorbilidad.

Se presentan las técnicas de Machine Learning, clúster difuso y reglas de asociación, aplicables a estudios longitudinales de tipo cohorte, en particular se aplica a una muestra de pacientes de entre 65 y 75 años durante los años 2010 a 2019, proveniente de la Cohorte EpiChron, de investigación en multimorbilidad, que contiene información de tipo médico sobre la población usuaria del sistema de salud público de Aragón.El objetivo final será la aplicación de estas técnicas para obtener una agrupación de enfermedades en diferentes clusters y construir trayectorias de multimorbilidad entre enfermedades que aparecen con un determinado patrón frecuente, es decir, determinar la secuencia temporal más probable en el diagnóstico.<br /