Clairvoyance: Intelligent Route Planning for Electric Buses Based on Urban Big Data

Nowadays many cities around the world have introduced electric buses to optimize urban traffic and reduce local carbon emissions. In order to cut carbon emissions and maximize the utility of electric buses, it is important to choose suitable routes for them. Traditionally, route selection is on the basis of dedicated surveys, which are costly in time and labor. In this paper, we mainly focus attention on planning electric bus routes intelligently, depending on the unique needs of each region throughout the city. We propose Clairvoyance, a route planning system that leverages a deep neural network and a multilayer perceptron to predict the future people's trips and the future transportation carbon emission in the whole city, respectively. Given the future information of people's trips and transportation carbon emission, we utilize a greedy mechanism to recommend bus routes for electric buses that will depart in an ideal state. Furthermore, representative features of the two neural networks are extracted from the heterogeneous urban datasets. We evaluate our approach through extensive experiments on real-world data sources in Zhuhai, China. The results show that our designed neural network-based algorithms are consistently superior to the typical baselines. Additionally, the recommended routes for electric buses are helpful in reducing the peak value of carbon emissions and making full use of electric buses in the city.

Conceptually Diverse Base Model Selection for Meta-Learners in Concept Drifting Data Streams

Meta-learners and ensembles aim to combine a set of relevant yet diverse base models to improve predictive performance. However, determining an appropriate set of base models is challenging, especially in online environments where the underlying distribution of data can change over time. In this paper, we present a novel approach for estimating the conceptual similarity of base models, which is calculated using the Principal Angles (PAs) between their underlying subspaces. We propose two methods that use conceptual similarity as a metric to obtain a relevant yet diverse subset of base models: (i) parameterised threshold culling and (ii) parameterless conceptual clustering. We evaluate these methods against thresholding using common ensemble pruning metrics, namely predictive performance and Mutual Information (MI), in the context of online Transfer Learning (TL), using both synthetic and real-world data. Our results show that conceptual similarity thresholding has a reduced computational overhead, and yet yields comparable predictive performance to thresholding using predictive performance and MI. Furthermore, conceptual clustering achieves similar predictive performances without requiring parameterisation, and achieves this with lower computational overhead than thresholding using predictive performance and MI when the number of base models becomes large.

Fast Electromagnetic Validations of Large-Scale Digital Coding Metasurfaces Accelerated by Recurrence Rebuild and Retrieval Method

The recurrence rebuild and retrieval method (R3M) is proposed in this paper to accelerate the electromagnetic (EM) validations of large-scale digital coding metasurfaces (DCMs). R3M aims to accelerate the EM validations of DCMs with varied codebooks, which involves the analysis of a group of similar but distinct coding patterns. The method transforms general DCMs to rigorously periodic arrays by replacing each coding unit with the macro unit, which comprises all possible coding states. The system matrix corresponding to the rigorously periodic array is globally shared for DCMs with arbitrary codebooks via implicit retrieval. The discrepancy of the interactions for edge and corner units are precluded by the basis extension of periodic boundaries. Moreover, the hierarchical pattern exploitation algorithm is leveraged to efficiently assemble the system matrix for further acceleration. Due to the fully utilization of the rigid periodicity, the computational complexity of R3M is theoretically lower than that of $\mathcal{H}$-matrix within the same paradigm. Numerical results for two types of DCMs indicate that R3M is accurate in comparison with commercial software. Besides, R3M is also compatible with the preconditioning for efficient iterative solutions. The efficiency of R3M for DCMs outperforms the conventional fast algorithms by a large margin in both the storage and CPU time cost.

Throughput Maximization for IRS-Aided MIMO FD-WPCN with Non-Linear EH Model

This paper studies an intelligent reflecting surface (IRS)-aided multiple-input-multiple-output (MIMO) full-duplex (FD) wireless-powered communication network (WPCN), where a hybrid access point (HAP) operating in FD broadcasts energy signals to multiple devices for their energy harvesting (EH) in the downlink (DL) and meanwhile receives information signals from devices in the uplink (UL) with the help of an IRS. Taking into account the practical finite self-interference (SI) and the non-linear EH model, we formulate the weighted sum throughput maximization optimization problem by jointly optimizing DL/UL time allocation, precoding matrices at devices, transmit covariance matrices at the HAP, and phase shifts at the IRS. Since the resulting optimization problem is non-convex, there are no standard methods to solve it optimally in general. To tackle this challenge, we first propose an element-wise (EW) based algorithm, where each IRS phase shift is alternately optimized in an iterative manner. To reduce the computational complexity, a minimum mean-square error (MMSE) based algorithm is proposed, where we transform the original problem into an equivalent form based on the MMSE method, which facilities the design of an efficient iterative algorithm. In particular, the IRS phase shift optimization problem is recast as an second-order cone program (SOCP), where all the IRS phase shifts are simultaneously optimized. For comparison, we also study two suboptimal IRS beamforming configurations in simulations, namely partially dynamic IRS beamforming (PDBF) and static IRS beamforming (SBF), which strike a balance between the system performance and practical complexity.

Incremental Ensemble Gaussian Processes

Belonging to the family of Bayesian nonparametrics, Gaussian process (GP) based approaches have well-documented merits not only in learning over a rich class of nonlinear functions, but also in quantifying the associated uncertainty. However, most GP methods rely on a single preselected kernel function, which may fall short in characterizing data samples that arrive sequentially in time-critical applications. To enable {\it online} kernel adaptation, the present work advocates an incremental ensemble (IE-) GP framework, where an EGP meta-learner employs an {\it ensemble} of GP learners, each having a unique kernel belonging to a prescribed kernel dictionary. With each GP expert leveraging the random feature-based approximation to perform online prediction and model update with {\it scalability}, the EGP meta-learner capitalizes on data-adaptive weights to synthesize the per-expert predictions. Further, the novel IE-GP is generalized to accommodate time-varying functions by modeling structured dynamics at the EGP meta-learner and within each GP learner. To benchmark the performance of IE-GP and its dynamic variant in the adversarial setting where the modeling assumptions are violated, rigorous performance analysis has been conducted via the notion of regret, as the norm in online convex optimization. Last but not the least, online unsupervised learning for dimensionality reduction is explored under the novel IE-GP framework. Synthetic and real data tests demonstrate the effectiveness of the proposed schemes.

Comparison of Neural Network based Soft Computing Techniques for Electromagnetic Modeling of a Microstrip Patch Antenna

This paper presents the comparison of various neural networks and algorithms based on accuracy, quickness, and consistency for antenna modelling. Using Nntool by MATLAB, 22 different combinations of networks and training algorithms are used to predict the dimensions of a rectangular microstrip antenna using dielectric constant, height of substrate, and frequency of operation as input. Comparison and characterization of networks is done based on accuracy, mean square error, and training time. Algorithms, on the other hand, are analyzed by their accuracy, speed, reliability, and smoothness in the training process. Finally, these results are analyzed, and recommendations are made for each neural network and algorithm based on uses, advantages, and disadvantages. For example, it is observed that Reduced Radial Bias network is the most accurate network and Scaled Conjugate Gradient is the most reliable algorithm for electromagnetic modelling. This paper will help a researcher find the optimum network and algorithm directly without doing time-taking experimentation.

Experimental Investigation on the Friction-induced Vibration with Periodic Characteristics in a Running-in Process under Lubrication

This paper investigated the friction-induced vibration (FIV) behavior under the running-in process with oil lubrication. The FIV signal with periodic characteristics under lubrication was identified with the help of the squeal signal induced in an oil-free wear experiment and then extracted by the harmonic wavelet packet transform (HWPT). The variation of the FIV signal from running-in wear stage to steady wear stage was studied by its root mean square (RMS) values. The result indicates that the time-frequency characteristics of the FIV signals evolve with the wear process and can reflect the wear stages of the friction pairs. The RMS evolvement of the FIV signal is in the same trend to the composite surface roughness and demonstrates that the friction pair goes through the running-in wear stage and the steady wear stage. Therefore, the FIV signal with periodic characteristics can describe the evolvement of the running-in process and distinguish the running-in wear stage and the stable wear stage of the friction pair.

Detecting Audio Adversarial Examples with Logit Noising

Automatic speech recognition (ASR) systems are vulnerable to audio adversarial examples that attempt to deceive ASR systems by adding perturbations to benign speech signals. Although an adversarial example and the original benign wave are indistinguishable to humans, the former is transcribed as a malicious target sentence by ASR systems. Several methods have been proposed to generate audio adversarial examples and feed them directly into the ASR system (over-line). Furthermore, many researchers have demonstrated the feasibility of robust physical audio adversarial examples(over-air). To defend against the attacks, several studies have been proposed. However, deploying them in a real-world situation is difficult because of accuracy drop or time overhead. In this paper, we propose a novel method to detect audio adversarial examples by adding noise to the logits before feeding them into the decoder of the ASR. We show that carefully selected noise can significantly impact the transcription results of the audio adversarial examples, whereas it has minimal impact on the transcription results of benign audio waves. Based on this characteristic, we detect audio adversarial examples by comparing the transcription altered by logit noising with its original transcription. The proposed method can be easily applied to ASR systems without any structural changes or additional training. The experimental results show that the proposed method is robust to over-line audio adversarial examples as well as over-air audio adversarial examples compared with state-of-the-art detection methods.

Data-Driven Market Segmentation in Hospitality Using Unsupervised Machine Learning

Within hospitality, marketing departments use segmentation to create tailored strategies to ensure personalized marketing. This study provides a data-driven approach by segmenting guest profiles via hierarchical clustering, based on an extensive set of features. The industry requires understandable outcomes that contribute to adaptability for marketing departments to make data-driven decisions and ultimately driving profit. A marketing department specified a business question that guides the unsupervised machine learning algorithm. Features of guests change over time; therefore, there is a probability that guests transition from one segment to another. The purpose of the study is to provide steps in the process from raw data to actionable insights, which serve as a guideline for how hospitality companies can adopt an algorithmic approach.

Sistema de sensoriamento sem fio aplicavel a deteccao de incendios florestais

In this research work, a hardware and software system is developed that uses wireless sensors to monitor environmental variables such as temperature, gas concentration and luminosity, in order to detect the existence of forest fires. Lora technology was used for wireless sensor networks with communication range that can reach on average up to 5km in urban areas and 10km in rural areas. The developed system also has an integrated web application (dashboard) and that in real time, collects data from wireless sensors, which together form the sensor module, also called device. Then, this data is presented on a map associ- ated with the positioning of each sensor module. The developed system was tested using practical experiments that used flames, gases and lighting, simulating the occurrence of fires. With the tests performed, it was observed the feasibility of the system, hardware/software developed, in detecting the fires in the simulated scenarios. Therefore, it was found that the research is promising, and may advance in the future for the detection of real fires.