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<p>Condition monitoring supported with artificial intelligence, cloud computing, and industrial internet of things (IIoT) technologies increases the feasibility of predictive maintenance (PdM). However, the cost of traditional sensors, data acquisition systems, and the information technology expert knowledge required to inform and implement PdM challenge the industry. This thesis proposes a hybrid condition monitoring system (CMS) architecture consisting of a distributed, low-cost IIoT-sensor solution. The CMS uses micro-electro-mechanical system (MEMS) microphones for data acquisition, edge computing for signal preprocessing, and cloud computing, including artificial neural networks (ANN) for higher-level information processing. The higher-level information processing includes condition detection and time-based prediction capabilities to inform PdM strategies. The system’s feasibility is validated using a testbed for reciprocating linear-motion axes.</p>

付記する学位プログラム名: デザイン学大学院連携プログラム / 京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24031号 / 情博第787号 / 新制||情||134(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 阿久津 達也, 教授 山本 章博, 教授 鹿島 久嗣 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Machine learning implementations in computer vision and speech recognition are wide and growing; both low- and high-level applications being required. This paper takes a look at the former and if basic implementations are good enough for real-world applications. To demonstrate this, a simple artificial neural network coded in Python and already existing libraries for Python are used to control a laser pointer via a servomotor and an Arduino, to create a voice-activated targeting system. The neural network trained on MNIST data consistently achieves an accuracy of 0.95 ± 0.01 when classifying MNIST test data, but also classifies captured images correctly if noise-levels are low. This also applies to the speech recognition, rarely giving wrong readings. The final prototype achieves success in all domains except turning the correctly classified images into targets that the Arduino can read and aim at, failing to merge the computer vision and speech recognition. / Maskininlärning är viktigt inom röstigenkänning och datorseende, för både små såväl som stora applikationer. Syftet med det här projektet är att titta på om enkla implementationer av maskininlärning duger för den verkligen världen. Ett enkelt artificiellt neuronnät kodat i Python, samt existerande programbibliotek för Python, används för att kontrollera en laserpekare via en servomotor och en Arduino, för att skapa ett röstaktiverat identifieringssystem. Neuronnätet tränat på MNIST data når en precision på 0.95 ± 0.01 när den försöker klassificera MNIST test data, men lyckas även klassificera inspelade bilder korrekt om störningen är låg. Detta gäller även för röstigenkänningen, då den sällan ger fel avläsningar. Den slutliga prototypen lyckas i alla domäner förutom att förvandla bilder som klassificerats korrekt till mål som Arduinon kan läsa av och sikta på, vilket betyder att prototypen inte lyckas sammanfoga röstigenkänningen och datorseendet.

Knowledge of species occurrence is a prerequisite for efficient and effective conservation and management. Unfortunately, knowledge of species occurrence is usually insufficient, so models that use environmental predictors and species occurrence records are used to predict species occurrence. Predicting the occurrence of stream fishes is often difficult because sampling data insufficiently describe species occurrence and important environmental conditions and predictive models insufficiently describe relations between species and environmental conditions. This dissertation 1) examines the sufficiency of fish species occurrence records at four spatial extents in Virginia, 2) compares modeling methods for predicting stream fish occurrence, and 3) assesses relations between species traits and model prediction characteristics. The sufficiency of sampling is infrequently addressed at the large spatial extents at which many management and conservation actions take place. In the first chapter of this dissertation I examine factors that determine the sufficiency of sampling to describe stream fish species richness at four spatial extents across Virginia using sampling simulations. Few regions of Virginia are sufficiently sampled, portending difficulty in accurately predicting fish species occurrence in most regions. The sufficient number of samples is often large and varies among regions and spatial scales, but it can be substantially reduced by reducing errors of sampling omission and increasing the spatial coverage of samples. Many methods are used to predict species occurrence. In the second chapter of this dissertation I compare the accuracy of the predictions of occurrence of seven species in each of three regions using linear discriminant function, generalized linear, classification tree, and artificial neural network statistical models. I also assess the efficacy of stream classification methods for predicting species occurrence. No modeling method proved distinctly superior. Species occurrence data and predictor data quality and quantity limited the success of predictions of stream fish occurrence for all methods. How predictive models are built and applied may be more important than the statistical method used. The accuracy, generality (transferability), and resolution of predictions of species occurrence vary among species. The ability to anticipate and understand variation in prediction characteristics among species can facilitate the proper application of predictions of species occurrence. In the third chapter of this dissertation I describe some conservation implications of relations between predicted occurrence characteristics and species traits for fishes in the upper Tennessee River drainage. Usually weak relations and variation in the strength and direction of relations among families precludes the accurate prediction of predicted occurrence characteristics. Most predictions of species occurrence have insufficient accuracy and resolution to guide conservation decisions at fine spatial grains. Comparison of my results with alternative model predictions and the results of many models described in peer-reviewed journals suggests that this is a common problem. Predictions of species occurrence should be rigorously assessed and cautiously applied to conservation problems. Collectively, the three chapters of this dissertation demonstrate some important limitations of models that are used to predict species occurrence. Model predictions of species occurrence are often used in lieu of sufficient species occurrence data. However, regardless of the method used to predict species occurrence most predictions have relatively low accuracy, generality and resolution. Model predictions of species occurrence can facilitate management and conservation, but they should be rigorously assessed and applied cautiously. / Ph. D.

Tire-pavement interaction is a dominant noise source for passenger cars and trucks above 25 mph (40 km/h) and 43 mph (70 km/h), respectively. For the same pavement, tires with different tread pattern and construction generate noise of different levels and frequencies. In the present study, forty-two different tires were tested over a range of speeds (45-65 mph, i.e., 72-105 km/h) on a non-porous asphalt pavement (a section of U.S. Route 460, both eastbound and westbound). An On-Board Sound Intensity (OBSI) system was instrumented on the test vehicle to collect the tire noise data at both the leading and trailing edge of the tire contact patch. An optical sensor recording the once-per-revolution signal of the wheel was also installed to monitor the vehicle speed and, more importantly, to provide the data needed to perform the order tracking analysis in order to break down the tire noise into two components. These two components are: the tread pattern and the non-tread pattern noise. Based on the experimental noise data collected, two artificial neural networks (ANN) were developed to predict the tread pattern (ANN1) and the non-tread pattern noise (ANN2) components, separately. The inputs of ANN1 are the coherent tread profile spectrum and the air volume velocity spectrum calculated from the digitized 3D tread pattern. The inputs of ANN2 are the tire size and tread rubber hardness. The vehicle speed is also included as input for the two ANN's. The optimized ANN's are able to predict the tire-pavement interaction noise well for different tires on the pavement tested. Another outcome of this work is the complete literature review on Tire-Pavement Interaction Noise (TPIN), as an appendix of this dissertation and covering ~1000 references, which might be the most comprehensive compilation of this topic. / PHD / A lot of people think the car noise is mostly from the engine, exhaust, or wind. However, this is not true. The noise in the exterior mainly comes from tires at over 25 mph. At normal highway speed, e.g., 60 mph, tire noise contributes over 70% of total noise. A quiet tire is desired for driving comfort. A number of attempts to reduce tire noise have been made in tire industries, including the tread pattern optimization and the tire structure design. In this work, a model was developed to predict the tire noise based on the tread pattern, tire size, tread rubber hardness, and vehicle speed. The model is called Artificial Neural Network Model of Tire-Pavement Interaction Noise (ANN Model of TPIN, or AMOT). This model is able to predict the noise contributions from the tread pattern and the pavement separately. Tire companies can use the model to design quite tires while customers can have an insight on choosing quite tires based on the tread patterns and/or tire structure.

The Von-Neumann bottleneck, a major challenge in computer architecture, results from significant data transfer delays between the processor and main memory. Crossbar arrays utilizing spin-based devices like Magnetoresistive Random Access Memory (MRAM) aim to overcome this bottleneck by offering advantages in area and performance, particularly for tasks requiring linear transformations. These arrays enable single-cycle and in-memory vector-matrix multiplication, reducing overheads, which is crucial for energy and area-constrained Internet of Things (IoT) sensors and embedded devices. This dissertation focuses on designing, implementing, and evaluating reconfigurable computation platforms that leverage MRAM-based crossbar arrays and analog computation to support deep learning and error resilience implementations. One key contribution is the investigation of Spin Torque Transfer MRAM (STT-MRAM) technology scaling trends, considering power dissipation, area, and process variation (PV) across different technology nodes. A predictive model for power estimation in hybrid CMOS/MTJ technology has been developed and validated, along with new metrics considering the Internet of Things (IoT) energy profile of various applications. The dissertation introduces the Spintronically Configurable Analog Processing in-memory Environment (SCAPE), integrating analog arithmetic, runtime reconfigurability, and non-volatile devices within a selectable 2-D topology of hybrid spin/CMOS devices. Simulation results show improvements in error rates, power consumption, and power-error-product metric for real-world applications like machine learning and compressive sensing, while assessing process variation impact. Additionally, it explores transportable approaches to more robust SCAPE implementations, including applying redundancy techniques for artificial neural network (ANN)-based digit recognition applications. Generic redundancy techniques are developed and applied to hybrid spin/CMOS-based ANNs, showcasing improved/comparable accuracy with smaller-sized networks. Furthermore, the dissertation examines hardware security considerations for emerging memristive device-based applications, discussing mitigation approaches against malicious manufacturing interventions. It also discusses reconfigurable computing for AI/ML applications based on state-of-the-art FPGAs, along with future directions in adaptive computing architectures for AI/ML at the edge of the network.

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This dissertation examines cross-category effects in consumer purchases from the big data and analytics perspectives. It uses data from Nielsen Consumer Panel and Scanner databases for its investigations. With big data analytics it becomes possible to examine the cross effects of many product categories on each other. The number of categories whose cross effects are studied is called category scale or just scale in this dissertation. The larger the category scale the higher the number of categories whose cross effects are studied. This dissertation extends research on models of cross effects by (1) examining the performance of MVP model across category scale; (2) customizing artificial neural network (ANN) techniques for large-scale cross effect analysis; (3) examining the performance of ANN across scale; and (4) developing a conceptual model of spending habits as a source of cross effect heterogeneity. The results provide researchers and managers new knowledge about using the two techniques in large category scale settings The computational capabilities required by MVP models grow exponentially with scale and thus are more significantly limited by computational capabilities than are ANN models. In our experiments, for scales 4, 8, 16 and 32, using Nielsen data, MVP models could not be estimated using baskets with 16 and more categories. We attempted to and could calibrate ANN models, on the other hand, for both scales 16 and 32. Surprisingly, the predictive results of ANN models exhibit an inverted U relationship with scale. As an ancillary result we provide a method for determining the existence and extent of non-linear own and cross category effects on likelihood of purchase of a category using ANN models. Besides our empirical studies, we draw on the mental budgeting model and impulsive spending literature, to provide a conceptualization of consumer spending habits as a source of heterogeneity in cross effect context. Finally, after a discussion of conclusions and limitations, the dissertation concludes with a discussion of open questions for future research.