Daily activity forecasts play an important role in the daily lives of residents in smart homes. Category forecasts and occurrence time forecasts of daily activity are two key tasks. Category forecasts of daily activity are correlated with occurrence time forecasts, however, existing research has only focused on one of the two tasks. Moreover, the performance of daily activity forecasts is low when the two tasks are performed in series. In this paper, a forecast model based on multi-task learning is proposed to forecast category and occurrence time of daily activity mutually and iteratively. Firstly, raw sensor events are pre-processed to form a feature space of daily activity. Secondly, a parallel multi-task learning model which combines a convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM) units are developed as the forecast model. Finally, five distinct datasets are used to evaluate the proposed model. The experimental results show that compared with the state-of-the-art single-task learning models, this model improves accuracy by at least 2.22%, and the metrics of NMAE, NRMSE and R2 are improved by at least 1.542%, 7.79% and 1.69%, respectively.
Over the past several years, the military has grown increasingly reliant upon the use of unattended aerial vehicles (UAVs) for surveillance missions. There is an increasing trend towards fielding swarms of UAVs operating as large-scale sensor networks in the air.1 Such systems tend to be used primarily for the purpose of acquiring sensory data with the goal of automatic detection, identification, and tracking objects of interest. These trends have been paralleled by advances in both distributed detection, 2 image/signal processing and data fusion techniques. Furthermore, swarmed UAV systems must operate under severe constraints on environmental conditions and sensor limitations. In this work, we investigate the effects of environmental conditions on target detection and recognition performance in a UAV network. We assume that each UAV is equipped with an optical camera, and use a realistic computer simulation to generate synthetic images. The detection algorithm relies on Haar-based features while the automatic target recognition (ATR) algorithm relies on Bessel K features. The performance of both algorithms is evaluated using simulated images that closely mimic data acquired in a UAV network under realistic environmental conditions. We design several fusion techniques and analyze both the case of a single observation and the case of multiple observations of the same target.
A companys ability to achieve its ultimate long-term goal highly depends on the potential and motives of its employees. The Human Resource Management team must be able to accurately evaluate a workers ability in quality work and incentivize him/her using promotion. While it has been quite difficult to measure an employees aptness for promotion, previously, machine learning classification methods has been researched upon. The aim of this study is to analyze a newly proposed hybrid classification model in detecting potential employee for promotion. The hybrid model combines the strength in feature selection of Artificial Neural Network model and the binary classification power of Support Vector Machine. The model is tested and trained after its hyperparameters are tuned. Afterward, the model is evaluated on the accuracy, precision, recall and F1 score, and compared to the individual SVM and ANN models for performance. The predictions of this proposed model have proven to exceed those two models with a range of 0.15% to 7.26% increase in performances. Note, the precision of ANN model is somewhat higher than the hybrid model. In summary, the hybrid ANN-SVM model has proven to work effectively with both imbalanced and balanced datasets.
UAV Based Distributed Automatic Target Detection Algorithm under Realistic Simulated Environmental Effects Shanshan Gong Over the past several years, the military has grown increasingly reliant upon the use of unattended aerial vehicles (UAVs) for surveillance missions. There is an increasing trend towards fielding swarms of UAVs operating as large-scale sensor networks in the air[1]. Such systems tend to be used primarily for the purpose of acquiring sensory data with the goal of automatic detection, identification, and tracking objects of interest. These trends have been paralleled by advances in both distributed detection [2], image/signal processing and data fusion techniques[3]. Furthermore, swarmed UAV systems must operate under severe constraints on environmental conditions and sensor limitations. In this work, we investigate the effects of environmental conditions on target detection performance in a UAV network. We assume that each UAV is equipped with an optical camera, and use a realistic computer simulation to generate synthetic images. The automatic target detector is a cascade of classifiers based on Haar-like features. The detector's performance is evaluated using simulated images that closely mimic data acquired in a UAV network under realistic camera and environmental conditions. In order to improve automatic target detection (ATD) performance in a swarmed UAV system, we propose and design several fusion techniques both at the image and score level and analyze both the case of a single observation and the case of multiple observations of the same target. First, I would like to thank Dr. Natalia Schmid for being such a patient and understanding thesis advisor. Her foresight, intuition, and care were instrumental in shaping this work. I have learned so much from her since I joined the Statistical Signal Processing Lab at West Virginia University. I also would like to thank my graduate committee members Dr. Xin Li and Dr. Matthew Valenti for their expert advice and support to my study and thesis. I must thank Xiaohan for her seemingly infinite supply of ideas and support for this work. I also thank Jinyu, Nathan and Francesco for their support and discussion which helped me so much on my study and research. Lastly, I thank my parents and my boyfriend Lei for always supporting my choice.
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