94 research outputs found
Deep learning and feature engineering techniques applied to the myoelectric signal for accurate prediction of movements
Técnicas de reconhecimento de padrões no Sinal Mioelétrico (EMG) são empregadas no
desenvolvimento de próteses robóticas, e para isso, adotam diversas abordagens de Inteligência Artificial (IA). Esta Tese se propõe a resolver o problema de reconhecimento de padrões
EMG através da adoção de técnicas de aprendizado profundo de forma otimizada. Para isso,
desenvolveu uma abordagem que realiza a extração da característica a priori, para alimentar
os classificadores que supostamente não necessitam dessa etapa. O estudo integrou a plataforma BioPatRec (estudo e desenvolvimento avançado de próteses) a dois algoritmos de
classificação (Convolutional Neural Network e Long Short-Term Memory) de forma híbrida,
onde a entrada fornecida à rede já possui características que descrevem o movimento (nível
de ativação muscular, magnitude, amplitude, potência e outros). Assim, o sinal é rastreado
como uma série temporal ao invés de uma imagem, o que nos permite eliminar um conjunto
de pontos irrelevantes para o classificador, tornando a informação expressivas. Na sequência,
a metodologia desenvolveu um software que implementa o conceito introduzido utilizando
uma Unidade de Processamento Gráfico (GPU) de modo paralelo, esse incremento permitiu
que o modelo de classificação aliasse alta precisão com um tempo de treinamento inferior a 1
segundo. O modelo paralelizado foi chamado de BioPatRec-Py e empregou algumas técnicas
de Engenharia de Features que conseguiram tornar a entrada da rede mais homogênea, reduzindo a variabilidade, o ruído e uniformizando a distribuição. A pesquisa obteve resultados
satisfatórios e superou os demais algoritmos de classificação na maioria dos experimentos
avaliados. O trabalho também realizou uma análise estatística dos resultados e fez o ajuste
fino dos hiper-parâmetros de cada uma das redes. Em última instancia, o BioPatRec-Py forneceu um modelo genérico. A rede foi treinada globalmente entre os indivíduos, permitindo
a criação de uma abordagem global, com uma precisão média de 97,83%.Pattern recognition techniques in the Myoelectric Signal (EMG) are employed in the
development of robotic prostheses, and for that, they adopt several approaches of Artificial
Intelligence (AI). This Thesis proposes to solve the problem of recognition of EMG standards
through the adoption of profound learning techniques in an optimized way. The research
developed an approach that extracts the characteristic a priori to feed the classifiers that
supposedly do not need this step. The study integrated the BioPatRec platform (advanced
prosthesis study and development) to two classification algorithms (Convolutional Neural
Network and Long Short-Term Memory) in a hybrid way, where the input provided to the
network already has characteristics that describe the movement (level of muscle activation,
magnitude, amplitude, power, and others). Thus, the signal is tracked as a time series instead
of an image, which allows us to eliminate a set of points irrelevant to the classifier, making the
information expressive. In the sequence, the methodology developed software that implements
the concept introduced using a Graphical Processing Unit (GPU) in parallel this increment
allowed the classification model to combine high precision with a training time of less than
1 second. The parallel model was called BioPatRec-Py and employed some Engineering
techniques of Features that managed to make the network entry more homogeneous, reducing
variability, noise, and standardizing distribution. The research obtained satisfactory results
and surpassed the other classification algorithms in most of the evaluated experiments. The
work performed a statistical analysis of the outcomes and fine-tuned the hyperparameters of
each of the networks. Ultimately, BioPatRec-Py provided a generic model. The network was
trained globally between individuals, allowing the creation of a standardized approach, with
an average accuracy of 97.83%
Simulation Intelligence: Towards a New Generation of Scientific Methods
The original "Seven Motifs" set forth a roadmap of essential methods for the
field of scientific computing, where a motif is an algorithmic method that
captures a pattern of computation and data movement. We present the "Nine
Motifs of Simulation Intelligence", a roadmap for the development and
integration of the essential algorithms necessary for a merger of scientific
computing, scientific simulation, and artificial intelligence. We call this
merger simulation intelligence (SI), for short. We argue the motifs of
simulation intelligence are interconnected and interdependent, much like the
components within the layers of an operating system. Using this metaphor, we
explore the nature of each layer of the simulation intelligence operating
system stack (SI-stack) and the motifs therein: (1) Multi-physics and
multi-scale modeling; (2) Surrogate modeling and emulation; (3)
Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based
modeling; (6) Probabilistic programming; (7) Differentiable programming; (8)
Open-ended optimization; (9) Machine programming. We believe coordinated
efforts between motifs offers immense opportunity to accelerate scientific
discovery, from solving inverse problems in synthetic biology and climate
science, to directing nuclear energy experiments and predicting emergent
behavior in socioeconomic settings. We elaborate on each layer of the SI-stack,
detailing the state-of-art methods, presenting examples to highlight challenges
and opportunities, and advocating for specific ways to advance the motifs and
the synergies from their combinations. Advancing and integrating these
technologies can enable a robust and efficient hypothesis-simulation-analysis
type of scientific method, which we introduce with several use-cases for
human-machine teaming and automated science
Pancreatic Cancer - Early Detection, Prognostic Factors, and Treatment
Background: Pancreatic cancer is the fourth leading cause of cancer-related death. Only about 6% of patients are alive 5 years after diagnosis. One reason for this low survival rate is that most patients are diagnosed at a late stage, when the tumor has spread to surrounding tissues or distant organs. Less than 20% of cases are diagnosed at an early stage that allows them to undergo potentially curative surgery. However, even for patients with a tumor that has been surgically removed, local and systemic recurrence is common and the median survival is only 17-23 months. This underscores the importance to identify factors that can predict postresection survival. With technical advances and centralization of care, pancreatic surgery has become a safe procedure. The future optimal treatment for pancreatic cancer is dependent on increased understanding of tumor biology and development of individualized and systemic treatment. Previous experimental studies have reported that mucins, especially the MUC4 mucin, may confer resistance to the chemotherapeutic agent gemcitabine and may serve as targets for the development of novel types of intervention. Aim: The aim of the thesis was to investigate strategies to improve management of pancreatic cancer, with special reference to early detection, prognostic factors, and treatment. Methods: In paper I, 27 prospectively collected serum samples from resectable pancreatic cancer (n=9), benign pancreatic disease (n=9), and healthy controls (n=9) were analyzed by high definition mass spectrometry (HDMSE). In paper II, an artificial neural network (ANN) model was constructed on 84 pancreatic cancer patients undergoing surgical resection. In paper III, we investigated the effects of transition from a low- to a high volume-center for pancreaticoduodenectomy in 221 patients. In paper IV, the grade of concordance in terms of MUC4 expression was examined in 17 tissue sections from primary pancreatic cancer and matched lymph node metastases. In paper V, pancreatic xenograft tumors were generated in 15 immunodeficient mice by subcutaneous injection of MUC4+ human pancreatic cancer cell lines; Capan-1, HPAF-II, or CD18/HPAF. In paper VI, a 76-member combined epigenetics and phosphatase small-molecule inhibitor library was screened against Capan-1 (MUC4+) and Panc-1 (MUC4-) cells, followed by high content screening of protein expression. Results/Conclusion: 134 differentially expressed serum proteins were identified, of which 40 proteins showed a significant up-regulation in the pancreatic cancer group. Pancreatic disease link associations could be made for BAZ2A, CDK13, DAPK1, DST, EXOSC3, INHBE, KAT2B, KIF20B, SMC1B, and SPAG5, by pathway network linkages to p53, the most frequently altered tumor suppressor in pancreatic cancer (I). An ANN survival model was developed, identifying 7 risk factors. The C-index for the model was 0.79, and it performed significantly better than the Cox regression (II). We experienced improved surgical results for pancreaticoduodenectomy after the transition to a high-volume center (≥25 procedures/year), including decreased operative duration, blood loss, hemorrhagic complications, reoperations, and hospital stay. There was also a tendency toward reduced operative mortality, from 4% to 0% (III). MUC4 positivity was detected in most primary pancreatic cancer tissues, as well as in matched metastatic lymph nodes (15/17 vs. 14/17), with a high concordance level (82%) (IV). The tumor incidence was 100% in the xenograft model. The median MUC4 count was found to be highest in Capan-1 tumors. α-SMA and collagen extent were also highest in Capan-1 tumors (V). Apicidin (a histone deacetylase inhibitor) had potent antiproliferative activity against Capan-1 cells and significantly reduced the expression of MUC4 and its transcription factor HNF4α. The combined treatment of apicidin and gemcitabine synergistically inhibited growth of Capan-1 cells (VI)
A sensory system for robots using evolutionary artificial neural networks.
The thesis presents the research involved with developing an Intelligent Vision System for an animat that can analyse a visual scene in uncontrolled environments. Inspiration was drawn both from Biological Visual Systems and Artificial Image Recognition Systems. Several Biological Systems including the Insect, Toad and Human Visual Systems were studied alongside popular Pattern Recognition Systems such as fully connected Feedforward Networks, Modular Neural Networks and the Neocognitron. The developed system, called the Distributed Neural Network (DNN) was based on the sensory-motor connections in the common toad, Bufo Bufo. The sparsely connected network architecture has features of modularity enhanced by the presence of lateral inhibitory connections. It was implemented using Evolutionary Artificial Neural Networks (EANN). A novel method called FUSION was used to train the DNN, which is an amalgamation of several concepts of learning in Artificial Neural Networks such as Unsupervised Learning, Supervised Learning, Reinforcement Learning, Competitive Learning, Self-organisation and Fuzzy Logic. The DNN has unique feature detecting capabilities. When the DNN was tested using images that comprised of combination of features used in the training set, the DNN was successful in recognising individual features. The combinations of features were never used in the training set. This is a unique feature of the DNN trained using Fusion that cannot be matched by any other popular ANN architecture or training method. The system proved to be robust in dealing with New and Noisy Images. The unique features of the DNN make the network suitable for applications in robotics such as obstacle avoidance and terrain recognition, where the environment is unpredictable. The network can also be used in the field of Medical Imaging, Biometrics (Face and Finger Print Recognition) and Quality Inspection in the Food Processing Industry and applications in other uncontrolled environments
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