This thesis proposes novel methods for identification and control of multivariable systems including multiple delays and describes their application to control of general anesthesia administration. First, an identification method for multivariable systems whose input and output paths have different time delays is presented. Second, a state predictor for multivariable systems whose input and output paths have different time delays is proposed. Third, the state predictor is used for constructing a state-predictive servo control system for controlled processes whose output paths have different time delays. A robust stability analysis method of the state-predictive servo control system is also examined. Furthermore, based on results of these theoretical studies, control systems for use in general anesthesia administration are developed. First, an identification method for multivariable systems whose input and output paths have different time delays is proposed. This method comprises two steps. In the first step, the delay lengths are estimated from the impulse response matrix identified from input and output (I/O) sequences using a subspace identification algorithm. In the second step, I/O sequences of a delay-free part are constructed from the original sequences and the delay estimates, and the system matrices of the delay-free part are identified. The proposed method is numerically stable and efficient. Moreover, it requires no complex optimization to obtain the delay estimates, nor does it require an assumption about the structure of the system matrices. Second, a state predictor is proposed for multivariable systems whose input and output paths have different time delays. The predictor consists of a full-order observer and a prediction mechanism. The former estimates a vector consisting of past states from the output. The latter predicts the current state from the estimated vector. The prediction error converges to zero at an arbitrary rate, which can be determined using pole assignment method, etc. In the proposed predictor, the interval length of the finite interval integration fed to the observer is shorter than that of an existing delay-compensating observer. Consequently, the proposed predictor is more numerically accurate than the delay-compensating observer. Using the proposed state predictor, a design method of a state-predictive servo controller is described for multivariable systems whose output paths have different time delays. Furthermore, a sufficient stability condition of the state-predictive servo control system against parameter mismatches is derived. Using a characteristic equation of the perturbed closed-loop system, a stability margin can be given on a plane whose axes correspond to the magnitudes of the mismatches on system matrices and on delay lengths. In the remainder of this thesis, development of anesthesia control systems is described to illustrate an application of the theoretical results described above. First, a hypnosis control system is presented. This system administers an intravenous hypnotic drug to regulate an electroencephalogram-derived index reflecting the patient’s hypnosis. The system comprises three functions: i) a model predictive controller that can take into account effects of time delay adequately, ii) an estimation function of individual parameters, and iii) a risk-control function for preventing undesirable states such as drug over-infusion or intra-operative arousal. Results of 79 clinical trials show that the system can reduce the total amount of drug infusion and maintain hypnosis more accurately than an anesthesiologist’s manual adjustment. Second, a simultaneous control system of hypnosis and muscle relaxation is described. For development of this system, a multivariable model of hypnosis and muscle relaxation is identified using the method proposed in this thesis. Then a state-predictive servo control system is designed for controlling hypnosis and muscle relaxation. Finally, the control system’s performance is evaluated through simulation. The resultant simultaneous control system satisfies the performance specifications of settling time, disturbance rejection ability, and a robust stability range. Although this system is not fully developed, the procedure of constructing this control system demonstrates the effectiveness of the proposed methods: the identification method for systems whose input and output paths have different time delays and the design and stability analysis methods of the state-predictive servo control system. / Kyoto University (京都大学) / 0048 / 新制・課程博士 / 博士(工学) / 甲第13820号 / 工博第2924号 / 新制||工||1432(附属図書館) / 26036 / UT51-2008-C736 / 京都大学大学院工学研究科電気工学専攻 / (主査)教授 小林 哲生, 教授 萩原 朋道, 准教授 古谷 栄光 / 学位規則第4条第1項該当

Teleoperation system is an important tool to control a device or model in an isolated area remotely where the operator cannot perform the task locally. The vast majority of teleoperation systems provides the operator with visual and haptic control to accomplish the assignment as naturally as possible. However, on a teleoperation system with considerable distance, the time delay could cause a drop in performance. This thesis aims to minimize delay problem by implementing a prediction approach using Kalman Filter. Kalman Filter algorithm has been widely used to estimate user movement for tracking systems. Kalman filter provides an efficient mechanism to predict future state based on Bayesian estimation to sequentially predict future states and measure an actual system to update system parameters. The primary objective of this work is to extract information generated by our prototyping model and visualizing the data to reflect the performance of the system. We use Phantom Omni devices and 3D arm as a model. Different type of Kalman filter algorithms is used to test the accuracy and performance of predicted state generated by the filter. The result shows that the implementation of Extended Kalman Filter (EKF) and smoothing function could overcome the networking delay on certain degrees. The comparison shows that the EKF has better accuracy and performance compared to Unscented Kalman Filter (UKF) when estimating the future state. Additionally, the implementation of smoothing function could improve the stability of teleoperation system. / Teleoperation systemet är ett viktigt verktyg för att styra en enhet eller modell i ett isolerat område på distans där operatören inte kan utföra uppgiften lokalt. De allra flesta av teleoperation system ger föraren visuell och haptisk kontroll för att utföra uppdraget så naturligt som möjligt. Men på en teleoperation system med stort avstånd, kan tidsfördröjningen medföra en nedgång i prestanda. Denna avhandling syftar till att minimera förseningar problem genom att implementera en förutsägelse tillvägagångssätt med Kalman Filter. Kalman filteralgoritm har i stor utsträckning används för att uppskatta användarens rörlighet för spårning. Kalman filter ger en effektiv mekanism för att förutsäga framtida stat grundad på Bayesian uppskattningen att sekventiellt förutsäga framtida tillstånd och mäta ett verkligt system för att uppdatera systemparametrar. Det primära syftet med detta arbete är att extrahera information som genereras av vår prototypmodell och visualisera data för att återspegla systemets prestanda. Vi använder Phantom Omni enheter och 3D-arm som en modell. Olika typer av Kalman filter algoritmer används för att testa riktigheten och prestandan hos förutsagda tillståndet genereras av filtret. Resultatet visar att genomförandet av Extended Kalman filter (EKF) och utjämningsfunktionen kan övervinna nätverk dröjsmålsvissa grader. Jämförelsen visar att EKF har bättre noggrannhet och prestanda jämfört med Unscented Kalman Filter (UKF) vid bedömningen av framtida tillstånd. Dessutom, genomförandet av utjämningsfunktionen skulle kunna förbättra stabiliteten hos teleoperation systemet.

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Šiame darbe tiriama galimybė analizuoti įtakas, lemiančias studentų mokymosi universitete sėkmę. Remiamasi duomenų gavybos algoritmais. Sukurtas būdas, kaip prognozuoti, ar būsimas studentas, remiantis jo turimais stojimo balais bei ankstesnės kartos patirtimi, sėkmingai užbaigs studijas. Pradžioje aptariamos galimos duomenų gavybos taikymo sritys, būtini etapai, tam skirta programinė įranga. Detalizuojami Microsoft SQL Server 2008 palaikomi duomenų gavybos algoritmai. Keturi iš jų sėkmingai pritaikyti pasirinktos dalykinės srities analizei. Sukurta analitinė sistema, sugebanti įvertinti stojimo balų įtakas, universitete dėstomų dalykų įtakas galimybei sėkmingai baigti studijas. Atliktas tyrimas, nustatyti, kuris duomenų gavybos algoritmas yra tinkamiausias prognozuoti studentų iškritimą. / This paper describes a research of evaluation of influences, causing a success to graduate university. The research is based on the data mining algorithms. There has been developed way to predict if a prospective student will successfully graduate the university or not. The prediction is based on the data of earlier generation of students, and school’s marks of prospective student. First part of paper describes the spheres, where data mining is adapted. Then, there is detailed stages used in data mining process; reviewed most popular data mining tools. After Microsoft SQL Server data mining algorithms were analyzed, it became clear witch ones are most suitable for the selected research area. The realization part explains how data mining can serve to improve the study process in university. This can be achieved by analyzing influences of different study disciplines to the ability to graduate the university. The last part of paper describes the performed experiment, witch showed the most appropriate algorithm to make predictions about ability to graduate the university.

This thesis is based on critical analysis of office development to determine the development potential of this real estate segment and therefore also predict the future evolution of market characteristics (supply, demand, vacancy rates, investments, rents, yields and prices) and a model scenarios of development towards an optimistic, realistic or pessimistic. The work includes a theoretical part, which defines the basic concepts needed to understand the problem. Furthermore, the development and current situation related to that topic and the actual forecast of the market for office space.

Travel-time information is essential in Advanced Traveler Information Systems (ATISs) and Advanced Traffic Management Systems (ATMSs). A key component of these systems is the prediction of the spatiotemporal evolution of roadway traffic state and travel time. From the perspective of travelers, such information can result in better traveler route choice and departure time decisions. From the transportation agency perspective, such data provide enhanced information with which to better manage and control the transportation system to reduce congestion, enhance safety, and reduce the carbon footprint of the transportation system. The objective of the research presented in this dissertation is to develop a framework that includes three major categories of methodologies to predict the spatiotemporal evolution of the traffic state. The proposed methodologies include macroscopic traffic modeling, computer vision and recursive probabilistic algorithms. Each developed method attempts to predict traffic state, including roadway travel times, for different prediction horizons. In total, the developed multi-tool framework produces traffic state prediction algorithms ranging from short – (0~5 minutes) to medium-term (1~4 hours) considering departure times up to an hour into the future. The dissertation first develops a particle filter approach for use in short-term traffic state prediction. The flow continuity equation is combined with the Van Aerde fundamental diagram to derive a time series model that can accurately describe the spatiotemporal evolution of traffic state. The developed model is applied within a particle filter approach to provide multi-step traffic state prediction. The testing of the algorithm on a simulated section of I-66 demonstrates that the proposed algorithm can accurately predict the propagation of shockwaves up to five minutes into the future. The developed algorithm is further improved by incorporating on- and off-ramp effects and more realistic boundary conditions. Furthermore, the case study demonstrates that the improved algorithm produces a 50 percent reduction in the prediction error compared to the classic LWR density formulation. Considering the fact that the prediction accuracy deteriorates significantly for longer prediction horizons, historical data are integrated and considered in the measurement update in the developed particle filter approach to extend the prediction horizon up to half an hour into the future. The dissertation then develops a travel time prediction framework using pattern recognition techniques to match historical data with real-time traffic conditions. The Euclidean distance is initially used as the measure of similarity between current and historical traffic patterns. This method is further improved using a dynamic template matching technique developed as part of this research effort. Unlike previous approaches, which use fixed template sizes, the proposed method uses a dynamic template size that is updated each time interval based on the spatiotemporal shape of the congestion upstream of a bottleneck. In addition, the computational cost is reduced using a Fast Fourier Transform instead of a Euclidean distance measure. Subsequently, the historical candidates that are similar to the current conditions are used to predict the experienced travel times. Test results demonstrate that the proposed dynamic template matching method produces significantly better and more stable prediction results for prediction horizons up to 30 minutes into the future for a two hour trip (prediction horizon of two and a half hours) compared to other state-of-the-practice and state-of-the-art methods. Finally, the dissertation develops recursive probabilistic approaches including particle filtering and agent-based modeling methods to predict travel times further into the future. Given the challenges in defining the particle filter time update process, the proposed particle filtering algorithm selects particles from a historical dataset and propagates particles using data trends of past experiences as opposed to using a state-transition model. A partial resampling strategy is then developed to address the degeneracy problem in the particle filtering process. INRIX probe data along I-64 and I-264 from Richmond to Virginia Beach are used to test the proposed algorithm. The results demonstrate that the particle filtering approach produces less than a 10 percent prediction error for trip departures up to one hour into the future for a two hour trip. Furthermore, the dissertation develops an agent-based modeling approach to predict travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in the decision making system, which predicts the travel time for each time interval according to past experiences from a historical dataset. A set of agent interactions are developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents with negligible weights with new agents. Consequently, the aggregation of each agent's recommendation (predicted travel time with associated weight) provides a macroscopic level of output – predicted travel time distribution. The case study demonstrated that the agent-based model produces less than a 9 percent prediction error for prediction horizons up to one hour into the future. / Ph. D.

Les systèmes embarqués sur puce (SoC) envahissent notre vie quotidienne. Avec les progrès technologiques, ils intègrent de plus en plus de fonctionnalités complexes impliquant des charges de calcul et des tailles de mémoire importantes. Alors que leur complexité est une tendance clé, la consommation d’énergie est aussi devenue un facteur critique pour la conception de SoC. Dans ce contexte, nous avons étudié une approche de modélisation au niveau transactionnel qui associe à un modèle fonctionnel SystemC-TLM une description d’une structure de gestion d’un arbre d’horloge décrit au même niveau d’abstraction. Cette structure développée dans une approche de séparation des préoccupations fournit à la fois l’interface pour la gestion de puissance des composants matériels et pour le logiciel applicatif. L’ensemble des modèles développés est rassemblé dans une librairie ClkARCH. Pour appliquer à un modèle fonctionnel un modèle d’un arbre d’horloge, nous proposons une méthodologie en trois étapes : spécification, modélisation et simulation. Une étape de vérification en simulation est aussi considérée basée sur des contrats de type assertion. De plus, nos travaux visent à être compatibles avec des outils de conception actuels. Nous avons proposé une représentation d’une structure de gestion d’horloge et de puissance dans le standard IP-XACT permettant de produire les descriptions C++ des structures de gestion de puissance du SoC. Enfin, nous avons proposé une approche de gestion de puissance basée sur l’observation globale des états fonctionnels du système dans le but d’éviter ainsi des prises de décisions locales peu efficaces à une optimisation de l’énergie. / Embedded systems-on-chip (SoC) invade our daily life. With advances in semiconductor technology, these systems integrate more and more complex and energy-intensive features which generate increasing computation load and memory size requirements. While the complexity of these systems is a key trend, energy consumption has emerged as a critical factor for SoC designers. In this context, we have studied a modeling transactional level approach allowing a description of a clock tree and its management structure to be associated with a functional model, both described at the same abstraction level. This structure developed in a separation of concerns approach provides both the interface to the power consumption management of the hardware components and the application software. All the models developed are gathered in a C++ ClkArch library. To apply to a SystemC-TLM architecture model a clock tree intent with its control part, we propose a methodology based on three steps: specification, modeling and simulation. A verification step based on simulation is also considered using contracts of assertion type. This work aims to build a modelling approach on current design tools. So we propose a representation of a clock and power management structure in the IP-XACT standard allowing a C++ description of the SoC power management structures to be generated. Finally, a power management strategy based on the global functional states of the components of the system architecture is proposed. This strategy avoids local decision-making unsuited to optimized overall power/energy management.

Vibrations are everywhere around us all the time and we often recognise them as sounds that we can hear and analyse with our brain. In this thesis, data that has been gathered from a diverter switch (DS) in a controlled environment, is analysed. This data consists of vibroacoustic measurements and information to indicate what is happening inside the DS as the vibroacoustic data is gathered. The frequency properties of vibroacoustic data from the DS gathered before this thesis are displayed using a wavelet transformation model. This means the frequency properties of the signal can be approximated for all times in the operation with a certain accuracy. As the DS is built from many different components, the frequency properties of these components are compared to the time-frequency picture of the full DS operation. This sort of comparison ends up not being feasible as the complexity of the DS frequency pattern is much more than that of a sum of its component’s frequency pattern. A second approach of analysing the gathered vibroacoustic data is by using a classification model. The information about what is happening inside of the DS is used to train a logistic regression model on different defined regions of the vibroacoustic data. Before the training is preformed though, the different defined regions are transformed into frequency space with help of the fast Fourier transform. With this, a classification model is produced, where vibroacoustic data of any time region can be fed into the model and the model will classify which defined region this vibroacoustic data belongs to. The results are promising, and the model can be used both for classification of the defined regions and potentially used to determine if the vibroacoustic properties of the DS has changed due to wear of the mechanical components or transformer oil.

Les pilotes d'avion sont constamment confrontés à des situations où ils doivent traiter des quantités importantes de données en très peu de temps, ce qui peut conduire à des erreurs. Nous avons créé un système de détection des écarts capable d'auditer le cockpit en temps réel pour détecter les actions qui ont été incorrectement ajoutées, omises ou qui n'ont pas été effectuées dans le bon ordre. Ce modèle évalue les écarts en se basant sur les données hiérarchiques des tâches trouvées dans le modèle de référence ontologique pour les procédures de pilotage, qui contient des procédures de référence basées sur la connaissance et rassemblées par des experts dans le domaine. Les actions des pilotes sont comparées aux séquences de référence de l'ontologie à l'aide de l'algorithme Needleman-Wunsch pour l'alignement global, ainsi que d'un réseau LSTM siamois. Une API pouvant être étendue à plusieurs simulateurs aérospatiaux, ainsi qu'un Runner, ont été créés pour permettre au Deviation Framework de se connecter au simulateur XPlane afin de surveiller le système en temps réel. Des données créées synthétiquement et contenant des mutations de séquences ont été analysées à des fins de test. Les résultats montrent que ce cadre est capable de détecter les erreurs ajoutées, omises et hors séquence. En outre, les capacités des réseaux siamois sont exploitées pour comprendre la relation de certaines anomalies de la chaîne de séquence afin qu'elles puissent être correctement ignorées (comme certaines tâches qui peuvent être exécutées dans le désordre par rapport à la séquence de référence). Les environnements de simulation enregistrant les données à une fréquence de 10 Hz, une valeur de 0.1 seconde constitue notre référence en temps réel. Ces évaluations de déviation peuvent être exécutées plus rapidement que notre contrainte de 0,1 seconde et ont été réalisées en 0,0179 seconde pour une séquence de décollage contenant 23 actions, ce qui est nettement plus performant que les modèles suivants de l'état de l'art. Les résultats de l'évaluation suggèrent que l'approche proposée pourrait être appliquée dans le domaine de l'aviation pour aider à détecter les erreurs avant qu'elles ne causent des dommages. \\En outre, nous avons formé un modèle d'apprentissage automatique pour reconnaître les signaux de pré-erreur dans le cortex cingulaire antérieur (CCA) à l'aide des données de test Flanker de l'ensemble de données COG-BCI, qui peuvent ensuite être utilisées pour détecter les états de pré-erreur chez les pilotes d'avion. Divers modèles d'apprentissage automatique ont été appliqués à l'ensemble de données, notamment des machines à vecteurs de support (SVM), des forêts aléatoires, un double modèle de réseau neuronal convolutif (CNN) et un modèle Transformer. Au-delà des conclusions typiques de l'étude, notre objectif s'étend à l'évaluation de l'applicabilité du modèle dans un domaine secondaire, à savoir l'évaluation du pouvoir discriminant des classificateurs pendant les procédures de décollage pour les pilotes d'avion. Les résultats de l'analyse de l'ensemble de données FLANKER ont révélé la supériorité du modèle transformateur, avec des réductions notables des faux négatifs et un score final macro moyen F1 de 0,610, et un score final macro moyen F1 de 0,578 sur les données pilotes. Comme nous prévoyons une augmentation des performances du classificateur avec davantage de données d'entraînement et des bandes d'interrogation étendues, cette étude jette les bases d'une recherche plus poussée sur la prédiction des états erronés et les modèles d'optimisation de l'apprentissage automatique pour les ICB et les applications du monde réel. / Aircraft pilots are constantly undergoing situations where they must process significant amounts of data in very small periods of time, which may lead to mistakes. We have created a deviation detection system that is capable of auditing the cockpit in real time to detect actions that have been incorrectly added, omitted, or done out of sequence. This model assesses deviations based on hierarchical task data found in the Ontological Reference Model for Piloting Procedures, which contains knowledge-based reference procedures assembled by experts in the domain. Pilot actions are compared to ontology reference sequences using the Needleman-Wunsch algorithm for global alignment, as well as a Siamese LSTM network. An API that can be expanded to several Aerospace Simulators, as well as a Runner, was created to enable the Deviation Framework to connect to the XPlane simulator for real-time system monitoring. Synthetically created data containing sequence mutations were analyzed for testing. The results show that this framework is capable of detecting added, omitted, and out of sequence errors. Furthermore, the capabilities of Siamese networks are leveraged to understand the relation of certain sequence chain anomalies so that they can correctly be ignored (such as certain tasks that can be performed out of order from the reference sequence). With simulation environments recording data at a frequency of 10Hz, a value of 0.1 seconds is our real-time benchmark. These deviation assessments are capable of being run faster than our 0.1 second requirement and have been clocked at 0.0179 seconds for one Takeoff sequence containing 23 actions - significantly outperforming the next state of the art models. The evaluation results suggest that the proposed approach could be applied in aviation settings to help catch errors before harm is done. \\Moreover, we have trained a machine learning model to recognize pre-error signals in the anterior cingulate cortex (ACC) using Flanker test data from the COG-BCI dataset, which can be subsequently employed to detect pre-error states in aviation pilots. Various machine learning models were applied to the dataset, including Support Vector Machines (SVM), Random Forests, double Convolutional Neural Network (CNN) model, and a Transformer model. Moving beyond typical study conclusions, our objective extends to assessing model applicability in a secondary domain —evaluating the classifiers' discriminative power during takeoff procedures for aviation pilots. Results from the analysis of the FLANKER dataset revealed the superiority of the transformer model, with notable reductions in false negatives and a final macro averaged F1 score of 0.610, and a final macro averaged F1 of 0.578 on the Pilot data. As we anticipate increases in classifier performance with more training data and extended polling bands, this study lays the groundwork for further research in erroneous state prediction and machine learning optimization models for BCI and real-world applications.