A fast and robust deep convolutional neural networks for complex human activity recognition using smartphone

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. As a significant role in healthcare and sports applications, human activity recognition (HAR) techniques are capable of monitoring humans’ daily behavior. It has spurred the demand for intelligent sensors and has been giving rise to the explosive growth of wearable and mobile devices. They provide the most availability of human activity data (big data). Powerful algorithms are required to analyze these heterogeneous and high-dimension streaming data efficiently. This paper proposes a novel fast and robust deep convolutional neural network structure (FR-DCNN) for human activity recognition (HAR) using a smartphone. It enhances the effectiveness and extends the information of the collected raw data from the inertial measurement unit (IMU) sensors by integrating a series of signal processing algorithms and a signal selection module. It enables a fast computational method for building the DCNN classifier by adding a data compression module. Experimental results on the sampled 12 complex activities dataset show that the proposed FR-DCNN model is the best method for fast computation and high accuracy recognition. The FR-DCNN model only needs 0.0029 s to predict activity in an online way with 95.27% accuracy. Meanwhile, it only takes 88 s (average) to establish the DCNN classifier on the compressed dataset with less precision loss 94.18%

An Efficient and Lightweight Deep Learning Model for Human Activity Recognition Using Smartphones

Traditional pattern recognition approaches have gained a lot of popularity. However, these are largely dependent upon manual feature extraction, which makes the generalized model obscure. The sequences of accelerometer data recorded can be classified by specialized smartphones into well known movements that can be done with human activity recognition. With the high success and wide adaptation of deep learning approaches for the recognition of human activities, these techniques are widely used in wearable devices and smartphones to recognize the human activities. In this paper, convolutional layers are combined with long short-term memory (LSTM), along with the deep learning neural network for human activities recognition (HAR). The proposed model extracts the features in an automated way and categorizes them with some model attributes. In general, LSTM is alternative form of recurrent neural network (RNN) which is famous for temporal sequences’ processing. In the proposed architecture, a dataset of UCI-HAR for Samsung Galaxy S2 is used for various human activities. The CNN classifier, which should be taken single, and LSTM models should be taken in series and take the feed data. For each input, the CNN model is applied, and each input image’s output is transferred to the LSTM classifier as a time step. The number of filter maps for mapping of the various portions of image is the most important hyperparameter used. Transformation on the basis of observations takes place by using Gaussian standardization. CNN-LSTM, a proposed model, is an efficient and lightweight model that has shown high robustness and better activity detection capability than traditional algorithms by providing the accuracy of 97.89%

Consumer Attitudes toward News Delivering: An Experimental Evaluation of the Use and Efficacy of Personalized Recommendations

This paper presents an experiment on newsreaders’ behavior and preferences on the interaction with online personalized news. Different recommendation approaches, based on consumption profiles and user location, and the impact of personalized news on several aspects of consumer decision-making are examined on a group of volunteers. Results show a significant preference for reading recommended news over other news presented on the screen, regardless of the chosen editorial layout. In addition, the study also provides support for the creation of profiles taking into consideration the evolution of user’s interests. The proposed solution is valid for users with different reading habits and can be successfully applied even to users with small consumption history. Our findings can be used by news providers to improve online services, thus increasing readers’ perceived satisfaction.Paula Viana and Márcio Soares were partial supported by Project “TEC4Growth—Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020”, under Research Line FourEyes, financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF). Paula Viana has also been supported by National Funds through the Portuguese funding agency, FCT—Fundação para a Ciência e a Tecnologia, within project UIDB/50014/2020. Rita Gaio was partially supported by CMUP, which is Financed by national funds through FCT—Fundação para a Ciência e a Tecnologia, I.P., under the project with the reference UIDB/00144/2020. Amílcar Correia was partially supported by the Project Pglobal (Nr. 2014/38592-Programa Operacional Temático Factores de Competitividade/Programa Operacional do Norte, Funded by ERDF).info:eu-repo/semantics/publishedVersio

Doing social media analytics

'The era of Big Data has begun' (boyd and Crawford, 2012: 662). In the few years since this statement, social media analytics has begun to accumulate studies drawing on social media as a resource and tool for research work. Yet, there has been relatively little attention paid to the development of methodologies for handling this kind of data. The few works that exist in this area often reflect upon the implications of 'grand' social science methodological concepts for new social media research (i.e. they focus on general issues such as sampling, data validity, ethics, etc). By contrast, we advance an abductively-oriented methodological suite designed to explore the construction of phenomena played out through social media. To do this, we use a software tool - Chorus - to illustrate a visual analytic approach to data. Informed by visual analytic principles, we posit a two-by-two methodological model of social media analytics, combining two data collection strategies with two analytic modes. We go on to demonstrate each of these four approaches ‘in action’, to help clarify how and why they might be used to address various research questions

Efficient use of deep learning and machine learning for load forecasting in South African power distribution networks

Abstract: Load forecasting, which is the act of anticipating future loads, has been shown to be important in power system network planning, operations and maintenance. Artificial Intelligence (AI) techniques have been shown to be good tools for load forecasting. Load forecasting can assist power distribution utilities maximise their revenue through optimising maintenance planning. With the dawn of the smart grid, first world countries have moved past the customer’s point of supply and use smart meters to forecast customer loads. These recent studies also utilise recent state of the art AI techniques such as deep learning techniques. Weather parameters are such as temperature, humidity and rainfall are usually used as parameters in these studies. South African load forecasting studies are outdated and recent studies are limited. Most of these studies are from 2010, and dating backwards to 1999. Hence they do not use recent state of the art AI techniques. The studies do not focus at distribution level load forecasting for optimal maintenance planning. The impact of adjusting power consumption data when there are spikes and dips in the data was not investigated in all these South African studies. These studies did not investigate the impact of weather parameters on different South African loads and hence load forecasting performance...D.Phil. (Electrical and Electronic Management

Mejoras de eficiencia computacional y precisión para sistemas predictivos de demanda eléctrica

Programa de Doctorado en Tecnologías Industriales y de TelecomunicaciónDebido a la inviabilidad del almacenamiento de energía eléctrica a gran escala, la energía eléctrica se genera y consume simultáneamente. En consecuencia, las entidades eléctricas necesitan sistemas de previsión de demanda para planificar operaciones y gestionar suministros. Predicciones de demanda precisas permiten el ahorro económico de suministros de generación de energía, así como reforzar la fiabilidad del abastecimiento a los consumidores. Por otra parte, las predicciones de demanda también permiten gestionar las energías renovables en las redes eléctricas, reduciendo indirectamente las emisiones de gases de efecto invernadero. Esta tesis se centra en mejorar, a escala peninsular, el sistema predictivo de Red Eléctrica de España (REE) y desarrollado por la Universidad Miguel Hernández (UMH). Se presenta un enfoque independiente de sus modelos matemáticos, ofreciendo metodologías aplicables a otros sistemas predictivos de otras redes eléctricas. Se exploran dos mejoras: una obtención determinista y automática del horario de cálculos y un preprocesado de datos de temperatura que da pie a análisis demográficos. Ambas mejoras también incrementan la precisión de las predicciones, siendo un criterio base de diseño. En Europa, debido a las directivas y las nuevas tecnologías, los sistemas de predicción pasan de trabajar en intervalos horarios a cuarto-horarios, lo que reduce el tiempo de cálculo y aumenta la carga computacional. Por lo tanto, un sistema predictivo puede no disponer de tiempo suficiente para calcular todos los pronósticos futuros. Los sistemas de predicción realizan cálculos a lo largo del día, repitiendo los mismos pronósticos a medida que se acerca la hora prevista. Sin embargo, hay predicciones que no son más precisas que otras ya calculadas, lo que da pie a no ejecutarlas y emplear las predicciones previas para ahorrar esfuerzo computacional y mantener la precisión. Con la idea de evitar cálculos contraproducentes, se desarrolla un algoritmo que estima qué pronósticos brindan mayor precisión que los anteriores, con lo que construye un horario de ejecuciones. El algoritmo se adapta a las necesidades computacionales y el sistema predictivo, con lo que se ha aplicado al sistema de predicción de REE, obteniendo un horario de ejecuciones que consigue una mayor precisión y se adapta a la carga computacional. Por otra parte, la demanda eléctrica depende de la temperatura ambiente por el uso de equipos de aire acondicionado y calefacción. Esta tesis propone un método automático de procesamiento y selección de variables térmicas con un doble objetivo: mejorar tanto la precisión como la interpretabilidad del sistema de pronóstico global. La metodología experimental se ha realizado con el sistema predictivo de REE. La nueva forma de trabajar con las temperaturas es interpretable, ya que separan el efecto de la temperatura según la ubicación y el tiempo mediante variables con un significado específico. Ambos estudios demuestran experimentalmente que las técnicas propuestas cumplen su cometido, mejorando la precisión y el coste computacional del sistema predictivo. También se observa que en España el calor tiene mayor influencia sobre la demanda que el frío. En los días calurosos, la temperatura del segundo día anterior tiene mayor influencia que la del anterior, mientras que en los días fríos ocurre lo contrario. A partir de la construcción del horario de ejecuciones se ha concluido que las temperaturas afectan poco a la demanda durante la madrugada; las previsiones de temperatura de menos de cuatro días de antelación implican una mayor precisión que las de más de cuatro; y que cuanto menor es la diferencia de tiempo entre el momento de predicción y el de ejecución, mayor precisión se tiene.Due to the infeasibility of large-scale electrical energy storage, electrical energy is generated and consumed simultaneously. Therefore, electricity entities need demand forecasting systems to plan operations and to manage supplies. Improving the forecasts accuracy allows economic savings of energy generation supplies, as well as reinforcing the reliability of energy supply to electricity consumers. In addition, demand forecasts allow renewable energies to be managed in electricity networks, indirectly reducing greenhouse gas emissions. This thesis focuses on improving, at peninsular scale, the forecasting system of Red Eléctrica de España (REE) developed by the Miguel Hernández University (UMH). An independent approach of mathematical models is presented, offering methodologies applicable to other forecasting systems from different electrical grids. Two improvements are tackled: a deterministic and automatic schedule obtention and a preprocessing of temperature data, which can be used as a tool for demographic studies. Both enhancements also increase the forecasting accuracy. In Europe, due to directives and new technologies, forecasting systems are transitioning from hourly intervals to quarter-hourly intervals, which reduces the calculation time and increases the computational burden. Therefore, a predictive system may not have enough time to compute all future forecasts. Forecasting systems perform calculations throughout the day, repeating the same forecasts while the forecast time approaches. However, there are predictions that are not more accurate than others already calculated, which leads to not executing them and using previous predictions to save computational effort and maintain accuracy. With the intention of avoiding counterproductive calculations, an algorithm is developed, that estimates which forecasts provide better accuracy than previous ones, then it builds a computing schedule. The algorithm adapts to the computational needs and the predictive system. It has been applied to the REE prediction system, obtaining a computing schedule that achieves greater precision and adapts to the computational load. Temperature affects electricity consumption through air conditioning and heating equipment. This thesis proposes an automatic method of processing and selecting variables with a double objective: to improve both the accuracy and the interpretability of the global forecasting system. The experimental methodology has been carried out with the REE predictive system. The new way of working with temperatures is interpretable as it separates the effect of temperature based on location and time, using variables with a specific meaning. Both studies experimentally demonstrate that the proposed techniques fulfill their purpose, improving the accuracy and computational cost of the predictive system. It is also observed that in Spain heat has a greater influence on demand than cold. On hot days, the temperature of the second previous day has a greater influence than that of the previous one, while on cold days the opposite occurs. Based on the construction of the execution schedule, it has been concluded that temperatures have reduced effect on demand during the early morning hours; temperature forecasts for less than four days ahead provide more accuracy than those more than four; and according as the time difference between the moment of prediction and the moment of execution decreases, the accuracy increases

筑波大学計算科学研究センター 平成30年度 年次報告書

まえがき ...... 21 センター組織と構成員 ...... 42 平成30 年度の活動状況 ...... 83 各研究部門の報告 ...... 15I. 素粒子物理研究部門 ...... 15II. 宇宙物理研究部門 ....... 40III. 原子核物理研究部門 ...... 65IV. 量子物性研究部門 ...... 83V. 生命科学研究部門 ...... 110 V-1. 生命機能情報分野 ...... 110 V-2. 分子進化分野 ...... 125VI. 地球環境研究部門 ...... 140VII. 高性能計算システム研究部門 ...... 155VIII. 計算情報学研究部門 ...... 207 VIII-1. データ基盤分野 ...... 207 VIII-2. 計算メディア分野 ...... 22

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

Future Transportation

Greenhouse gas (GHG) emissions associated with transportation activities account for approximately 20 percent of all carbon dioxide (co2) emissions globally, making the transportation sector a major contributor to the current global warming. This book focuses on the latest advances in technologies aiming at the sustainable future transportation of people and goods. A reduction in burning fossil fuel and technological transitions are the main approaches toward sustainable future transportation. Particular attention is given to automobile technological transitions, bike sharing systems, supply chain digitalization, and transport performance monitoring and optimization, among others