Yang Gu

TOSELM: Timeliness Online Sequential Extreme Learning Machine

For handling data and training model, existing machine learning methods do not take timeliness problem into consideration. Timeliness here means the data distribution or the data trend changes with time passing by. Based on timeliness management scheme, a novel machine learning algorithm Timeliness Online Sequential Extreme Learning Machine (TOSELM) is proposed, which improves Online Sequential Extreme Learning Machine (OSELM) with central tendency and dispersion characteristics of data to deal with timeliness problem. The performance of proposed algorithm has been validated on several simulated and realistic datasets, and experimental results show that TOSELM utilizing adaptive weight scheme and iteration scheme can achieve higher learning accuracy, faster convergence and better stability than other machine learning methods.

Feature Adaptive Online Sequential Extreme Learning Machine for lifelong indoor localization

AbstractnWi-Fi-based indoor localization with high capability and feasibility needs to implement lifelong online learning mechanism. However, the characteristic of Wi-Fi is wide variability, which lies in not only the fluctuation of signal strength value, but also the increase or decrease in the number of access points (APs). The traditional algorithms are effective for signal fluctuation, but cannot handle the dimension-changing problem of features caused by increase and decrease in APs’ number. To solve this problem, we propose a Feature Adaptive Online Sequential Extreme Learning Machine (FA-OSELM) algorithm. It can transfer the original model to a new one with a small number of data with new features, so as to make the new model suitable for the new feature dimension. The experiments show that the FA-OSELM can get higher accuracy with a small amount of new data, and it is an effective method to make lifelong indoor localization practical.

Semi-supervised deep extreme learning machine for Wi-Fi based localization

Along with the proliferation of mobile devices and wireless signal coverage, indoor localization based on Wi-Fi gets great popularity. Fingerprint based method is the mainstream approach for Wi-Fi indoor localization, for it can achieve high localization performance as long as labeled data are sufficient. However, the number of labeled data is always limited due to the high cost of data acquisition. Nowadays, crowd sourcing becomes an effective approach to gather large number of data; meanwhile, most of them are unlabeled. Therefore, it is worth studying the use of unlabeled data to improve localization performance. To achieve this goal, a novel algorithm Semi-supervised Deep Extreme Learning Machine (SDELM) is proposed, which takes the advantages of semi-supervised learning, Deep Leaning (DL), and Extreme Learning Machine (ELM), so that the localization performance can be improved both in the feature extraction procedure and in the classifier. The experimental results in real indoor environments show that the proposed SDELM not only outperforms other compared methods but also reduces the calibration effort with the help of unlabeled data.

Constraint Online Sequential Extreme Learning Machine for lifelong indoor localization system

As an important technology in LBS (Location Based Services) field, Wi-Fi based indoor localization suffers signal fluctuation problem which prevents lifelong and high performance running. With the fluctuation of wireless signal over time, fingerprints collected at the same location become different; therefore existing model cannot fit the new collected data well, which decreases the localization accuracy. In this paper, a novel indoor localization method COSELM (Constraint Online Sequential Extreme Learning Machine) is proposed, utilizing incremental data to update the old model and overcome the fluctuation problem. The performance of COSELM is validated in real Wi-Fi indoor environment. Compared with OSELM, it can improve more than 5% localization accuracy on average; and in contrast to batch learning, COSELM can save more than 50% time consumption.

OCEAN: a new opportunistic computing model for wearable activity recognition

Activities of Daily Living (ADL) recognition through wearable devices is an emerging research field. While, for many applications, recognition methods are faced with simultaneously dynamic changes in feature dimension, activity class and data distribution. Existing approaches mainly handle at most one of these three challenges, which significantly affects their performance. In this paper, we propose an Opportunistic Computing model for wEarable Activity recognitioN (OCEAN); by fusing random mapping, fuzzy clustering, and weight updating techniques, OCEAN can online adaptively adjust Single-hidden Layer Feedforward neural networks connection, structure and weight in a coherent manner. Experimental evaluations demonstrate that OCEAN improves the recognition accuracy by 5% to 15% compared to traditional approaches towards dynamic changes.

A smart scarf for pulse signal monitoring using a flexible pressure nanosensor

Real-time, long-term pulse signal monitoring plays a significant role in monitoring chronic diseases for elder people, pregnant women etc. For users with weak pulse, a carotid pulse signal monitoring system can be appropriate. However, till today no unobtrusive solution is available. Therefore, we suggest a novel flexible and planar pressure nanosensor weaved in a smart scarf system for pulse signal monitoring with better user experience called Smart-SP (smart scarf for pulse signal monitoring system). To our knowledge, this is the first time applying a flexible pressure nanosensor on a wearable carotid pulse monitoring system. To meet the needs of the application, a method is proposed to design the size of the sensor. An interface allows third-party analysis software and provides raw data. A database of pulse signal for diagnostic purposes is set-up. Digital low-pass filter improves the signal accuracy.

Real-Time and Accurate Indoor Localization with Fusion Model of Wi-Fi Fingerprint and Motion Particle Filter

As the development of Indoor Location Based Service (Indoor LBS), a timely localization and smooth tracking with high accuracy are desperately needed. Unfortunately, any single method cannot meet the requirement of both high accuracy and real-time ability at the same time. In this paper, we propose a fusion location framework with Particle Filter using Wi-Fi signals and motion sensors. In this framework, we use Extreme Learning Machine (ELM) regression algorithm to predict position based on motion sensors and use Wi-Fi fingerprint location result to solve the error accumulation of motion sensors based location occasionally with Particle Filter. The experiments show that the trajectory is smoother as the real one than the traditional Wi-Fi fingerprint method.

Online deep intelligence for Wi-Fi indoor localization

Indoor localization based on Wi-Fi is crucial for many practical applications. However, considered the highly dynamic indoor environment, Wi-Fi indoor localization system cannot maintain the high performance for longtime. To address this challenge, we propose a novel online deep learning approach OSDELM, which guarantees the running time of localization system from two aspects: discriminative feature, and updated model. Specifically, deep learning helps extract discriminative Wi-Fi features, and online learning updates the out-of-date model to fit the new environment. The experiments in real indoor environment show that the proposed OSDELM method can cope with the highly dynamic indoor environment issue and make the localization system work well in online manner.

FSELM: fusion semi-supervised extreme learning machine for indoor localization with Wi-Fi and Bluetooth fingerprints

Recently, the problem of indoor localization based on WLAN signals is attracting increasing attention due to the development of mobile devices and the widespread construction of networks. However, no definitive solution for achieving a low-cost and accurate positioning system has been found. In most traditional approaches, solving the indoor localization problem requires the availability of a large number of labeled training samples, the collection of which requires considerable manual effort. Previous research has not provided a means of simultaneously reducing human calibration effort and improving location accuracy. This paper introduces fusion semi-supervised extreme learning machine (FSELM), a novel semi-supervised learning algorithm based on the fusion of information from Wi-Fi and Bluetooth Low Energy (BLE) signals. Unlike previous semi-supervised methods, which consider multiple signals individually, FSELM fuses multiple signals into a unified model. When applied to sparsely calibrated localization problems, our proposed method is advantageous in three respects. First, it can dramatically reduce the human calibration effort required when using a semi-supervised learning framework. Second, it utilizes fused Wi-Fi and BLE fingerprints to markedly improve the location accuracy. Third, it inherits the beneficial properties of ELMs with regard to training and testing speeds because the input weights and biases of hidden nodes can be generated randomly. As demonstrated by experimental results obtained on practical indoor localization datasets, FSELM possesses a better semi-supervised manifold learning ability and achieves higher location accuracy than several previous batch supervised learning approaches (ELM, BP and SVM) and semi-supervised learning approaches (SELM, S-RVFL and FS-RVFL). Moreover, FSELM needs less training and testing time, making it easier to apply in practice. We conclude through experiments that FSELM yields good results when applied to a multi-signal-based semi-supervised learning problem. The contributions of this paper can be summarized as follows: First, the findings indicate that effective multi-data fusion can be achieved not only through data-layer fusion, feature-layer fusion and decision-layer fusion but also through the fusion of constraints within a model. Second, for semi-supervised learning problems, it is necessary to combine the advantages of different types of data by optimizing the model’s parameters.

Heterogeneous Data Driven Manifold Regularization Model for Fingerprint Calibration Reduction

Accurate indoor localization is very important for various kinds of Location Based Services (LBS). For most of traditional approaches, the location estimation problems assume the availability of a vast amount of labeled calibrated data, which requires a great deal of manual effort. Previous researches cannot deal with this problem in both calibration reduction, location accuracy. In this paper, we propose a heterogeneous data driven manifold regularization model known as HeterMan to calibration-effort reduction for tracking a mobile node in a wireless sensor network. With the constraint of user heading orientation, we build a mapping function between signal space, physical space with extremely less labeled data, a large amount of unlabeled data. Experimental results show that we can achieve high accuracy with extremely less calibration effort comparing with previous methods. Furthermore, our method can reduce computation complexity, time consumption by parallel processing while maintaining high accuracy.