Alumni
Human Activity Recognition (HAR) plays a significant role in the Ambient Assisted Living (AAL) system, which aims to provide sustainable healthcare for an aging population and those with special needs. HAR automatically categorizes people’s activi ties while they wear wearable sensors. With an effective HAR system, we should be able to monitor the behavior of individuals as well as their activities and issue specific warn ings as necessary. The goal of this paper is to propose a methodological framework for developing the HAR model based on an application of Long Short-Term Memory (LSTM) network. We investigated the model selection and parameters based on Cross Validation (CV) and learning rate optimization across two well-known public HAR datasets, Mo biAct and WISDM. An analysis of the CV variance becomes a considerable impact on the generalization of the model’s learning capability. The relationship between the CV variance and accuracy can be used to guide the selection of the fold number in k-fold CV. Our studies had shown the scientific evidence and technical guidance for solving the HAR problem with improvements not only in the proposed model’s accuracy and AUC of more than 99% on average, but also in its generalization performance, which could be useful for future related studies.
In this paper, a study of the novel technique based on Fuzzy Inference Sys tem (FIS) for storm eye identification has been presented. The ocean wind vectors are provided by the NASA QuikSCAT satellite to predict the significance of tropical cycloge nesis. This database is slightly noisy, incomplete and indirect. For this reason, the cloud satellite image can be an alternative option. However, the cloud shape may be ambiguous, which can introduce a long search time. As a result, utilizing combined information from both resources can lead to a reduction in resource deficiency. The FIS is used to describe the uncertain behavior of the complex system consisting of several factors. It provides ability to model the dynamic behavior of the storm and designates the best candidate eye position in the region of interest. Then, the spiral cloud model is adopted to enhance the search results in order to achieve the accurate eye position. The experimental results are conducted based on six reference storms. The proposed system offers higher flexibility in analyzing the storm eye position with the minimum average distance error of 92.8 km and approximately 16.25% less average distance error compared to the reference. This demonstrates the significant performance improvement in detecting the eye location of the storm.
In this paper, the performance of the cyclone identification system using histogram of wind speed and wind direction from the QuikSCAT satellite is demonstrated. The detections based on support vector machines (SVM) classification and Fuzzy C-Means (FCM) clustering are evaluated. SVM technique makes use of a kernel function for classification, which performs well with datasets having nonlinear boundaries. However, it is difficult to determine the suitable kernel function for each dataset and it is needed to be examined. On the other hand, FCM technique is soft unsupervised clustering, which allows each data element to be in more than one cluster with different membership value. This makes it robust to ambiguity datasets. A database of 90 events; 45 cyclone events and 45 non-cyclone events; from the QuikSCAT satellite data is used for the performance evaluation. The performance of the proposed cyclone identification system is then compared to that of [7]. The experimental results show that cyclone identification using Fuzzy C-Mean clustering outperforms that using SVM technique since the SVM is sensitive to the outliers or noises in the dataset thus leads to a reduction in identification performance.
This paper presents an automatic typhoon eye identification using combined features from QuikSCAT satellite and spiral cloud image. Using only cloud information may lead to excessive time to achieve the search solution if encountering ambiguous cloud shape. Therefore, QuikSCAT wind information is used to estimate the candidate region of interest (ROI) and eye location in order to restrain searching range of spiral cloud. The candidate eye location is further expanded to search for the best eye location using the spiral curve model (SCM). The experimental results demonstrate significant improvement in the eye location identification with approximately 60.5% reduction in distance error compared to the three references.
Identifying typhoon eye location is quite a challenging task since several factors are needed to be evaluated. With deficient information from a single resource, it may lead to disappointed results. The wind information from satellite provides great ability in tropical cyclone intensity estimation, however, lacking in sufficient data to be analyzed in the blank swaths area due to its non-overlapped orbit. Moreover, it is sometimes noisy, incomplete and indirect. In addition to satellite information, the cloud image is an alternative choice. However, the uncertain cloud shape can result in undesirable excessive search time. In order to improve the detection efficiency, a novel heuristic search is proposed to automatically detect the typhoon eye location using the statistics of wind parameters from QuikSCAT satellite and spiral cloud images. The heuristic search is employed to find the best candidate eye location in the region of interest (ROI) obtained from QuikSCAT wind information. This offers great ability to restrain searching range of spiral cloud detection. The candidate eye location is further expanded in order to search for the best eye location using the SCM. The proposed technique can achieve approximately 64.4 % decrease in distance error compared to the three references. This can demonstrate significant enhancement in detecting the location of the typhoon eye.
This paper presents an indoor navigation system based on multi-destination path planning and WiFi fingerprint localization. A user is allowed to specify multiple destinations and can detour the route at any time. Path planning will automatically update path using 2-opt and A* algorithms. The revised route will be analyzed according to user's current position supplied from the WiFi RSS fingerprint positioning. Naïve Bayes classification is adopted to learn from the RSS fingerprint priors stored in the database. Extensive experiments are conducted and performance comparison is analyzed and demonstrates significant performance improvement and higher noise tolerance with integration of the probabilistic priors. It can be seen that the proposed system enables user experience for indoor navigation service with support for automatic route updating and navigation refinement according to localization.
