When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available. However, as the number of tasks grows, storing and retraining on such data becomes infeasible. A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable. A more surprising observation is that Learning without Forgetting may be able to replace fine-tuning with similar old and new task datasets for improved new task performance.

Learning without Forgetting

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私の computer 環境

WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.

WiFi-SLAM Using G aussian Process Latent Variable Models

As the technology associated to the ?Web Services? trend gains significant adoption, the need for a corresponding design approach becomes increasingly important. This paper introduces a foundational model for designing (composite) services. The innovation of this model lies in the identification of four interrelated viewpoints (interface behaviour, provider behaviour, choreography, and orchestration) and their formalization from a control-flow perspective in terms of Petri nets. By formally capturing the interrelationships between these viewpoints, the proposal enables the static verification of the consistency of composite services designed in a cooperative and incremental manner. A proof-of-concept simulation and verification tool has been developed to test the possibilities of the proposed model.

Service-oriented Design : A Multi-viewpoint Approach

Location service is one of the primary services in smart automated systems of Internet of Things (IoT). For various location-based services, accurate localization has become a key issue. Recently, research on IoT localization systems for smart buildings has been attracting increasing attention. In this paper, we propose a novel localization approach that utilizes the neighbor relative received signal strength to build the fingerprint database and adopts a Markov-chain prediction model to assist positioning. The approach is called the novel localization method (LNM) in short. In the proposed LNM scheme, the history data of the pedestrian’s locations are analyzed to further lower the unpredictable signal fluctuations in a smart building environment, meanwhile enabling calibration-free positioning for various devices. The performance evaluation conducted in a realistic environment shows that the presented method demonstrates superior localization performance compared with well-known existing schemes, especially when the problems of device heterogeneity and WiFi signals fluctuation exist.

Enhanced Fingerprinting and Trajectory Prediction for IoT Localization in Smart Buildings

In the era of mobile internet, Location Based Services (LBS) have developed dramatically. Seamless Indoor and Outdoor Navigation and Localization (SNAL) has attracted a lot of attention. No single positioning technology was capable of meeting the various positioning requirements in different environments. Selecting different positioning techniques for different environments is an alternative method. Detecting the users’ current environment is crucial for this technique. In this paper, we proposed to detect the indoor/outdoor environment automatically without high energy consumption. The basic idea was simple: we applied a machine learning algorithm to classify the neighboring Global System for Mobile (GSM) communication cellular base station’s signal strength in different environments, and identified the users’ current context by signal pattern recognition. We tested the algorithm in four different environments. The results showed that the proposed algorithm was capable of identifying open outdoors, semi-outdoors, light indoors and deep indoors environments with 100% accuracy using the signal strength of four nearby GSM stations. The required hardware and signal are widely available in our daily lives, implying its high compatibility and availability.

Indoor-Outdoor Detection Using a Smart Phone Sensor.

A team of autonomous decision-making robots can be employed for some critical tasks, such as disaster detection, plant protection, and military reconnaissance. The use of such team of robots to perform well is a challenging problem. To address the challenge, a decentralized multi-robot patrolling problem in uncertain environments without prior knowledge is investigated in this paper. First, the patrolling environment is modeled as an undirected graph, and information is attached to the vertex, that follows an unknown discrete Markov chain. Each robot patrols independently within a particular area, where these areas may intersect each other. Robots coordinate their actions periodically to gather as much valuable information as possible. Then, the patrolling problem is cast as a Bayes-adaptive transition-decoupled partially observable Markov decision process formulation. However, it is intractable to solve the multi-robot patrolling problem with complex interactions. To solve the problem, a scalable decentralized online learning and planning algorithm is proposed by extending the Monte Carlo tree search method, which computes patrols that are bounded optimal. In the proposed algorithm, each local look-ahead tree has a low branching factor and the coordinated manner is not complicated. In addition, we benchmark the proposed algorithm against some state-of-the-art online solvers to empirically assess the performance. The results show that the proposed algorithm is able to achieve high performance in typical decentralized patrolling scenarios.

Bayesian Reinforcement Learning for Multi-Robot Decentralized Patrolling in Uncertain Environments

With the prevalence of net-centric environments, Service-Oriented Architectures (SOAs) have emerged as a paradigm that greatly impacts the modeling and simulation (M&S) community. This paper has two interrelated goals. The first is to give a comprehensive review of various service-oriented simulation frameworks to help researchers select the appropriate one for their specific purpose. The second goal is to combine the common features derived from the review into one unifying framework that can describe and prescribe various specific approaches. The focus of this paper is on the common functionalities of service-oriented simulations reflected in the review and unifying framework. In particular, we emphasize the way SOAs and M&S are combined, and the interoperability and composability challenges of distributed M&S services. We describe some fundamental concepts first. Then we present a comprehensive survey of several classical frameworks, including formalism-based, model-driven, interoperability protocol based, eXtensible Modeling and Simulation Framework (XMSF), Open Grid Services Architecture (OGSA) based, and ontology-driven frameworks. Based on the review, we propose a novel three-dimensional reference model that can unify the ad hoc approaches into a common framework. The model can be used as a guideline or an analytic means to find potential and possible future directions. In particular, the model inspects the crossover between the disciplines of M&S, service-orientation, and software/systems engineering. Based on the model, we present a detailed comparison of the reviewed frameworks. Finally the significance of the paper is discussed and future directions are recommended.

Service-oriented simulation framework: An overview and unifying methodology

The widespread deployment of Wi-Fi communication makes it easy to find Wi-Fi access points in the indoor environment, which enables us to use them for Wi-Fi fingerprint positioning. Although much research is devoted to this topic in the literature, the practical implementation of Wi-Fi based localization is hampered by the variations of the received signal strength (RSS) due to e.g. impediments in the channel, decreasing the positioning accuracy. In order to improve this accuracy, we integrate Pedestrian Dead Reckoning (PDR) with Wi-Fi fingerprinting: the movement distance and walking direction, obtained with the PDR algorithm, are combined with the K-Weighted Nearest Node (KWNN) algorithm to assist in selecting reference points (RPs) closer to the actual position. To illustrate and evaluate our algorithm, we collected the RSS values from 8 Wi-Fi access points inside a building to create a fingerprint database. Simulation results showed that, compared to the conventional KWNN algorithm, the positioning algorithm is improved with 17 %, corresponding to an average positioning error of 1.58 m for the proposed algorithm, while an accuracy of 1.91 m was obtained with the KWNN algorithm. The advantage of the proposed algorithm is that not only the existing Wi-Fi infrastructure and fingerprint database can be used without modification, but also that a standard mobile phone is sufficient to implement our algorithm.

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Wi-Fi Fingerprint Positioning Updated by Pedestrian Dead Reckoning for Mobile Phone Indoor Localization

Recently, service-oriented paradigm is emerging as a new pattern following process-oriented and object-oriented ones in systems analysis and software development. Service-oriented High Level Architecture (SOHLA) refers to the high level architecture (HLA) enabled by Service-Oriented Architecture (SOA) and Web Services etc. techniques which supports distributed interoperating services. The detailed comparisons between HLA and SOA are made to illustrate the importance of their combination. Then several key enhancements and changes of HLA Evolved Web Service API are introduced in comparison with native APIs, such as Federation Development and Execution Process, communication mechanisms, data encoding, session handling, testing environment and performance analysis. Some approaches are summarized including Web-Enabling HLA at the communication layer, HLA interface specification layer, federate interface layer and application layer. Finally the problems of current research are discussed, and the future directions are pointed out.

Weiping Wang

Papers

Indoor-Outdoor Detection Using a Smart Phone Sensor.

Bayesian Reinforcement Learning for Multi-Robot Decentralized Patrolling in Uncertain Environments

Service-oriented simulation framework: An overview and unifying methodology

Wi-Fi Fingerprint Positioning Updated by Pedestrian Dead Reckoning for Mobile Phone Indoor Localization

Service-oriented high level architecture