Adaptive Speech Quality Aware Complex Neural Network for Acoustic Echo Cancellation with Supervised Contrastive Learning

Acoustic echo cancellation (AEC) is designed to remove echoes, reverberation, and unwanted added sounds from the microphone signal while maintaining the quality of the near-end speaker's speech. This paper proposes adaptive speech quality complex neural networks to focus on specific tasks for real-time acoustic echo cancellation. In specific, we propose a complex modularize neural network with different stages to focus on feature extraction, acoustic separation, and mask optimization receptively. Furthermore, we adopt the contrastive learning framework and novel speech quality aware loss functions to further improve the performance. The model is trained with 72 hours for pre-training and then 72 hours for fine-tuning. The proposed model outperforms the state-of-the-art performance.Comment: Submitted to International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2023. Under revie

Availability Allocation of Networked Systems Using Markov Model and Heuristics Algorithm

It is a common practice to allocate the system availability goal to reliability and maintainability goals of components in the early design phase. However, the networked system availability is difficult to be allocated due to its complex topology and multiple down states. To solve these problems, a practical availability allocation method is proposed. Network reliability algebraic methods are used to derive the availability expression of the networked topology on the system level, and Markov model is introduced to determine that on the component level. A heuristic algorithm is proposed to obtain the reliability and maintainability allocation values of components. The principles applied in the AGREE reliability allocation method, proposed by the Advisory Group on Reliability of Electronic Equipment, and failure rate-based maintainability allocation method persist in our allocation method. A series system is used to verify the new algorithm, and the result shows that the allocation based on the heuristic algorithm is quite accurate compared to the traditional one. Moreover, our case study of a signaling system number 7 shows that the proposed allocation method is quite efficient for networked systems

Estimating the Cost of Engineering Services using Parametrics and the Bathtub Failure Model

In the engineering domain, customers traditionally purchase a product by paying a one-off price to the supplier. Currently, customers are increasingly demanding engineering services in different disciplines, such as in the aerospace, defence, manufacturing and construction sectors. This means that the customer may buy a product, which includes an integrated service or purchase the usage of a product/service (i.e. availability and capability) rather than the ownership of a product. To meet this demand for engineering services rather than stand-alone products, many companies have moved from providing a tangible product to offering such services. In both academia and industry, the majority of the activities have focused on estimating the cost for products with little in the area of estimating the cost of providing engineering services. There appears to be a clear knowledge gap in the field of costing models and rules for providing such services. It is this gap in knowledge, which is the focus of the research presented in this thesis. This research is focused on estimating the cost for engineering services using parametrics and the bathtub failure model. This is illustrated through the application to a Chinese manufacturing and service provider. Eight years of cost-related data such as historical bills, service charges, maintenance records, and costs for storage has been collected. Observations, questionnaires and structured meetings have been conducted within the company. A methodology and a cost model for estimating the cost for engineering services are provided. The major contribution of this research is the creation of an approach, which is to estimate the cost of engineering services using parametrics and the bathtub failure model.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Modeling and analysis of a semi-active magneto-rheological damper suspension seat and controller synthesis

Whole body vibration in operational vehicles can cause serious musculo-skeletal disorders among the exposed workers. Consequently, considerable efforts have been made to protect vehicle operators from potentially harmful vibration. This thesis was aimed at the development of a semi-active suspension seat equipped with a magneto-rheological (MR) fluid damper. A damper controller was synthesized to minimize the vibration transmitted to the seated body and the frequency of end-stop impacts, which is known to induce high intensity vibration or shock motions to the seated occupant. A suspension seat was modeled by considering the kinematic non-linearity due to the cross-linkages and the damper link, while the cushion characteristics were linearized about the operating preload. The force-velocity properties of the MR damper were modeled by piecewise polynomial functions of applied current on the basis of the laboratory-measured data. The kineto-dynamic model of the suspension seat was thoroughly validated using the laboratory-measured responses under harmonic excitations in the 0.5 to 10Hz range. The performance characteristics of the passive suspension seat model were evaluated under different vehicular excitations in terms of frequency-weighted rms acceleration, vibration dose value (VDV), seat effective amplitude transmissibility (SEAT) and VDV ratio. These performance characteristics are also evaluated under amplified vehicular excitations in order to investigate the frequency as well as the potential suppression of end-stop impacts. The controller synthesis was realized in two stages: (1) attenuation of continuous vibration; and (2) suppression of end-stop impacts. Two different algorithms were explored in the first stage synthesis, which included a sky-hook control algorithm and a relative states feedback control algorithm. Each algorithm was further utilized in two different control current modulations. The performance potentials of each control synthesis were investigated using the 2 MATLAB Simulink platform under harmonic, transient, and random vehicular excitations in terms of SEAT and VDV ratio. One controller design (overall best suited for implementations) was subsequently implemented in a hardware-in-the-loop (HIL) test platform coupled with a MR-fluid damper mounted on an electro-hydraulic actuator that was linked to the HIL simulation platform. The semi-active suspension seat performance characteristics were further evaluated under different excitations using the selected control scheme. The results showed that the selected control scheme yielded SEAT and VDV ratio reductions in the 5 to 30% range depending upon the nature of excitations. The implementation of the second-stage controller, which was tested only by simulations, entirely eliminated the occurrence of end-stop impacts at nominal vibration level and attenuated the end-stop impact severity of three times amplified excitations by up to 10% . The results further suggested that the use of MR-fluid damper in suspension seat was most beneficial to city buses and class I earth moving vehicles amongst the selected inputs

Minimalist Traffic Prediction: Linear Layer Is All You Need

Traffic prediction is essential for the progression of Intelligent Transportation Systems (ITS) and the vision of smart cities. While Spatial-Temporal Graph Neural Networks (STGNNs) have shown promise in this domain by leveraging Graph Neural Networks (GNNs) integrated with either RNNs or Transformers, they present challenges such as computational complexity, gradient issues, and resource-intensiveness. This paper addresses these challenges, advocating for three main solutions: a node-embedding approach, time series decomposition, and periodicity learning. We introduce STLinear, a minimalist model architecture designed for optimized efficiency and performance. Unlike traditional STGNNs, STlinear operates fully locally, avoiding inter-node data exchanges, and relies exclusively on linear layers, drastically cutting computational demands. Our empirical studies on real-world datasets confirm STLinear's prowess, matching or exceeding the accuracy of leading STGNNs, but with significantly reduced complexity and computation overhead (more than 95% reduction in MACs per epoch compared to state-of-the-art STGNN baseline published in 2023). In summary, STLinear emerges as a potent, efficient alternative to conventional STGNNs, with profound implications for the future of ITS and smart city initiatives.Comment: 9 page

A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data

Endowing dialogue systems with personas is essential to deliver more human-like conversations. However, this problem is still far from well explored due to the difficulties of both embodying personalities in natural languages and the persona sparsity issue observed in most dialogue corpora. This paper proposes a pre-training based personalized dialogue model that can generate coherent responses using persona-sparse dialogue data. In this method, a pre-trained language model is used to initialize an encoder and decoder, and personal attribute embeddings are devised to model richer dialogue contexts by encoding speakers' personas together with dialogue histories. Further, to incorporate the target persona in the decoding process and to balance its contribution, an attention routing structure is devised in the decoder to merge features extracted from the target persona and dialogue contexts using dynamically predicted weights. Our model can utilize persona-sparse dialogues in a unified manner during the training process, and can also control the amount of persona-related features to exhibit during the inference process. Both automatic and manual evaluation demonstrates that the proposed model outperforms state-of-the-art methods for generating more coherent and persona consistent responses with persona-sparse data.Comment: Long paper accepted at AAAI 202

Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services

Graph Neural Networks (GNNs) have gained growing interest in miscellaneous applications owing to their outstanding ability in extracting latent representation on graph structures. To render GNN-based service for IoT-driven smart applications, traditional model serving paradigms usually resort to the cloud by fully uploading geo-distributed input data to remote datacenters. However, our empirical measurements reveal the significant communication overhead of such cloud-based serving and highlight the profound potential in applying the emerging fog computing. To maximize the architectural benefits brought by fog computing, in this paper, we present Fograph, a novel distributed real-time GNN inference framework that leverages diverse and dynamic resources of multiple fog nodes in proximity to IoT data sources. By introducing heterogeneity-aware execution planning and GNN-specific compression techniques, Fograph tailors its design to well accommodate the unique characteristics of GNN serving in fog environments. Prototype-based evaluation and case study demonstrate that Fograph significantly outperforms the state-of-the-art cloud serving and fog deployment by up to 5.39x execution speedup and 6.84x throughput improvement.Comment: Accepted by IEEE/ACM Transactions on Networkin

FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout

Federated Learning (FL) requires frequent exchange of model parameters, which leads to long communication delay, especially when the network environments of clients vary greatly. Moreover, the parameter server needs to wait for the slowest client (i.e., straggler, which may have the largest model size, lowest computing capability or worst network condition) to upload parameters, which may significantly degrade the communication efficiency. Commonly-used client selection methods such as partial client selection would lead to the waste of computing resources and weaken the generalization of the global model. To tackle this problem, along a different line, in this paper, we advocate the approach of model parameter dropout instead of client selection, and accordingly propose a novel framework of Federated learning scheme with Differential parameter Dropout (FedDD). FedDD consists of two key modules: dropout rate allocation and uploaded parameter selection, which will optimize the model parameter uploading ratios tailored to different clients' heterogeneous conditions and also select the proper set of important model parameters for uploading subject to clients' dropout rate constraints. Specifically, the dropout rate allocation is formulated as a convex optimization problem, taking system heterogeneity, data heterogeneity, and model heterogeneity among clients into consideration. The uploaded parameter selection strategy prioritizes on eliciting important parameters for uploading to speedup convergence. Furthermore, we theoretically analyze the convergence of the proposed FedDD scheme. Extensive performance evaluations demonstrate that the proposed FedDD scheme can achieve outstanding performances in both communication efficiency and model convergence, and also possesses a strong generalization capability to data of rare classes

A logical description of metaphor analysis

This paper alms to use logical techniques to describe how metaphors are analyzed. Metaphor analysis process functions as one of the most important strategies to uncover implied information in discourse understanding. A metaphor analysis logic system is developed and presented in terms of its dcfmitions, axiomatic system, inference rules, properties. semantic interpretations and applications. The merits of the logic are that possible worlds are substituted with possible feature spaces compared with Local Frame Theory, and an understanding modal operator U-p, a relational symbol < and a Gestalt rule are embodied. The most notable feature of the logic is that it takes into account subjective factors in the process of metaphor analysis