Visual landmarks, with their unique textures, play a crucial role in vision-based environmental sensing applications (such as augmented reality-based advertising, indoor navigation). However, few studies have focused on the construction of visual landmark databases, which is usually time-consuming and laborious. We propose a novel method called NeRF-VLD, which first introduces Neural Radiance Fields (NeRF) to build visual landmark databases efficiently. NeRF-VLD generates images from a small number of sources, thus eliminating the need for extensive site surveys and consequently reducing deployment costs significantly. To improve the quality of generated images, we introduce two scene-constrained modules. In the first module, sparse depth maps provided by Structure-from-Motion are introduced as additional geometric constraints. In the second module, scene regularization is applied to image pixels where depth information is unavailable, constraining the volume density of sample points near the camera plane to alleviate ghostly artifacts problem. Experimental results demonstrate that NeRF-VLD can achieve comparable landmark identification and positioning accuracy to real-image databases while reducing the number of image acquisition by 97%. Compared with full manual survey, NeRF-VLD reduces survey and update costs and improves the applicability of vision-based service.

This article focuses on the accuracy and practicality of geomagnetic-based indoor localization in large-scale real-world sites. Previous studies often use continuous geomagnetic sequences for better discriminability but ignore that longer sequences, while more accurate, increase response times. Shorter sequences enhance practicality but sacrifice accuracy. Additionally, varying walking speeds can cause sequence variations along the same path, leading to ambiguity. To address these challenges, we propose a spatial-temporal constrained indoor localization model (STC-loc) that enables both accurate and practical localization using geomagnetic sequences collected at arbitrary walking speeds. Specifically, we first design a speed-oriented adaptive normalization module to tackle the walking speed heterogeneity problem. Then, to balance accuracy and practicality, we initially employ shorter sequences as input and a self-attention hierarchical structure to efficiently extract the temporal features for initial location estimation. Subsequently, we further apply an encoder-decoder-based refinement strategy, leveraging spatial contextual for continuous localization to ensure accuracy and robustness. Additionally, a slide-window-based two-phase system architecture optimizes processing efficiency and scalability. Experiments on three large-scale real-world sites have demonstrated the superiority of the proposed STC-loc, reducing the localization error by more than 43% with shorter geomagnetic sequences.

Deep neural networks are widely employed in various inertial measurement units (IMUs) based motion analysis systems, including activity recognition, user authentication, and healthcare. However, the security risks of current IMU systems have not been thoroughly studied. In this paper, we present a backdoor attack for IMU sequences. We first introduce methods for generating poisoned samples using patching and fusion procedures for IMUs. Despite the effectiveness, patching and fusion requires pre-determined starting position in IMU sequences and is distinguishing from the rest. To address these limitations, we further propose a novel joint optimization scheme for generating position-independent IMU patterns that can attack at arbitrary positions in the IMU sequence. Furthermore, we incorporate the penalty of geometrical consistency into the optimization and encourage stealthy perturbations as IMU patterns. We validate our proposed backdoor attack in two dominant tasks of IMU-based motion analysis systems: human activity recognition (HAR) and user authentication (UA). Experimental results demonstrate that IMU systems are prone to be attacked by optimized IMU patterns (attack success rate up to 90% at extremely low positioning rate 2%) across different deep neural network architectures and two datasets on both tasks, highlighting the security risks inherent in current IMU systems.

Indoor location-based services (LBS) hold significant potential for social and commercial value in smart city development. However, fingerprint-based indoor localization technologies face challenges due to the dynamic indoor environment. Updating the fingerprint database through dense site surveys is costly. Therefore, a method utilizing sparse samples (10%) is attractive. This work proposes the fingerprint database adaptation paradigm (FDAP), an adaptive update framework designed for sparse samples. The FDAP creates a swin transformer-based reconstruction model (STRM), effectively addressing the issue of inaccurate map reconstruction from sparse samples. Using the STRM significantly reduces the expenses associated with updating the fingerprint database. Additionally, FDAP evaluates the quality of the samples with a signal quality evaluation algorithm (SQEA), assessing the features of dispersion degree, signal strength, and fingerprint correlation from sparse samples. Lastly, FDAP provides a signal correction algorithm (SCA) based on partial least squares regression, which minimizes potential errors for unreliable reconstruction signals. Experiments conducted on a public dataset demonstrate that FDAP effectively accommodates radio map variations over time. It reduces the reconstruction error by 22.2 % compared to the current state-of-the-art method. The code is publicly available at https://github.com/Cen-Shaoqi/FDAP.

Employing low-cost inertial measurement units (IMUs) from off-the-shelf mobile devices, inertial odometry techniques can provide environment-independent position information, exhibiting great research and commercial value. However, the high noise level of low-cost inertial sensor readings still makes this challenging. To address this, we propose a deep inertial odometry method that employs hierarchical temporal features of IMU sequences. Specifically, the proposed method transforms inertial odometry problem into a seq2seq translation task by segmenting the overall inertial sequence into subsequences, which are referred to as raw sentences. Then an attention-based hierarchical structure is designed to extract and fuse multi-level temporal features, generating feature sequences with rich contextual information, which are referred to as source sentences. Finally, we utilize the state-of-the-art Transformer as a translator for estimating corresponding pose change sequences, which are referred to as target sentences, and integrate the estimation results into the trajectory. We have conducted extensive experiments on two public datasets: the small-scale OxIOD and the large-scale IDOL. The experimental results demonstrate that our method reduces the mean absolute trajectory error and relative trajectory error by at least 16.8% and 16.7%, respectively, on the OxIOD dataset, and by 48.4% and 58.1%, respectively, on the IDOL dataset compared to competing schemes.

Of all indoor localization techniques, vision-based localization emerges as a promising one, mainly due to the ubiquity of rich visual features. Visual landmarks, which present distinguishing textures, play a fundamental role in visual indoor localization. However, few researches focus on visual landmark labeling. Preliminary arts usually designate a surveyor to select and record visual landmarks, which is tedious and time-consuming. Furthermore, due to structural changes (e.g., renovation), the visual landmark database may be outdated, leading to degraded localization accuracy. To overcome these limitations, we propose VILL , a user-friendly, efficient, and accurate approach for visual landmark labeling. VILL asks a user to sweep the camera to take a video clip of his/her surroundings. In the construction stage, VILL identifies unlabeled visual landmarks from videos adaptively according to the graph-based visual correlation representation. Based on the spatial correlations with selected anchor landmarks, VILL estimates locations of unlabeled ones on the floorplan accurately. In the update stage, VILL formulates an alteration identification model based on the judgments from different users to identify altered landmarks accurately. Extensive experimental results in two different trial sites show that VILL reduces the site survey substantially (by at least 65.9%) and achieves comparable accuracy.

Geomagnetism-based indoor localization has great social and commercial value due to its pervasiveness and indepen-dence from extra infrastructure. To improve the distinguishability of geomagnetic signals as location clues, geomagnetic sequences are usually taken as input. Although longer input sequence can provide higher localization accuracy, it suffers from high response time in practice. To address the above, we first utilize short geomagnetic sequences as input, alleviating high response time, and propose an efficient single position estimation model, taking advantage of modified transformer to estimate position for each independent short sequence. Noticing the temporal dependency and the spatial consistency constraint during continuous positioning, we further propose a joint position estimation model to capture the correlations among consecutive short sequences, achieving higher accuracy with multiple short sequences. We have conducted extensive experiments in two typical trial sites, a narrow office area and a spacious parking lot. Experimental results show that the proposed approach outperforms state-of-the-art competing schemes, and the localization error is reduced by more than 32% with shorter geomagnetic sequences.

Downward-viewing fisheye cameras have attracted much attention in surveillance systems due to the wide coverage and less occlusion. However, pedestrian detection in downward-viewing fisheye cameras remains an open problem due to a lack of large-scale labeled dataset. Furthermore, it's time-consuming and labor-intensive to label a downward-viewing fisheye dataset manually. To address this, we propose a self-bootstrapping pedestrian detection method, which automatically pseudo-labels downward-viewing fisheye images by making full use of spatial and temporal consistency of pedestrians in the cameras to improve the accuracy of pedestrian detection. We segment the downward-viewing fisheye images into two regions and propose the pseudo-labeling methods for them progressively: a cyclic fine-tuned detector for the oblique region and a visual tracking method for the vertical region. Combining the pseudo-labels from two regions, we fine-tune the network for better accuracy. Experimental results show that the proposed approach reduces time consumption by about 95% compared with the labor-intensive manual labeling while it still reaches competitive and comparable Average Precision (AP).

Wind farm layout optimization(WFLO) is a largescale binary optimization problem which is difficult to solve by conventional optimization methods. On the other hand, due to the wake effect, the power generation efficiency of the wind farm can be greatly reduced, which increases the complexity of the WFLO problem. This paper proposes a binary negatively correlated search (BNCS), which extends the evolutionary logic of negatively correlated search and generates binary variables using a rounding transfer function. Numerical results confirm that the proposed BNCS can achieve good performance for both Knapsack and WFLO problem.

The combined emission economic dispatch (CEED) problem has become an increasingly important issue due to the detrimental environment impact of burning extensive fossil fuels in conventional thermal power generation plants. Furthermore, the maturity of technologies has led to rapid progress worldwide in electric power generation using clean pollution-free and renewable energy resources such as the wind and solar power generations. This paper presents a restart covariance matrix adaption evolution strategy with increasing population size (IPOP-CMAES) for solving the wind-integrated power systems considering both emissions and economic costs, which have extended the algorithm from the benchmark functions to the practical application in the power system and got the outstanding performance. The IPOP-CMAES launches a restart strategy with the increasing population when the stopping criterion is met on the basic CMAES. The IPOP-CMAES is tested on a 10-unit system with different levels of wind power penetrations and a 40-unit system. The results show that it can provide higher quality solutions compared to literature algorithms and other commonly used methods no matter in the small or large scale power systems.

We propose an attention-injective deformable convolutional network called ADCrowdNet for crowd understanding that can address the accuracy degradation problem of highly congested noisy scenes. ADCrowdNet contains two concatenated networks. An attention-aware network called Attention Map Generator (AMG) first detects crowd regions in images and computes the congestion degree of these regions. Based on detected crowd regions and congestion priors, a multi-scale deformable network called Density Map Estimator (DME) then generates high-quality density maps. With the attention-aware training scheme and multi-scale deformable convolutional scheme, the proposed ADCrowdNet achieves the capability of being more effective to capture the crowd features and more resistant to various noises. We have evaluated our method on four popular crowd counting datasets (ShanghaiTech, UCF_CC_50, WorldEXPO'10, and UCSD) and an extra vehicle counting dataset TRANCOS, and our approach beats existing state-of-the-art approaches on all of these datasets.

Indoor localization has attracted considerable attention lately, due to its large commercial and social values in smart cities. The existing indoor localization approaches mostly rely on fingerprint techniques, and many of those leverage either spatially discrete fingerprints or temporally consecutive ones for localization, which either suffers from large errors due to signal ambiguities or high time overhead with long sequences. To achieve high accuracy with low computational cost, we propose ST-Loc, a deep neural network that extracts features from multiple representations of a single signal sequence for localization, where each representation indicates a corresponding signal structure with underlying feature correlations. Taking geomagnetism as an example, we infer location features from two different representations, e.g., spatial and temporal. In spatial representation, a signal sequence is converted to a signal heatmap, where each pixel corresponds to a spatial location and the value indicates fingerprint. Temporal representation, on the other hand, is a signal sequence with ordered readings, which provides temporal correlations. Using these different representations, we employ convolutional and recurrent networks to extract location features and fuse them to generate more distinguishing features for localization. We have conducted extensive experiments in two different trial sites, a narrow office area and a spacious food plaza. Our experimental results show that ST- Loc achieves more than 43% average localization error reduction compared with state-of-the-art competing schemes in both trial sites. © 2019 IEEE.

Image-based indoor localization has aroused much interest recently because it requires no infrastructure support. Previous approaches on image-based localization, due to their computation and storage requirements, often process queries at servers. This does not scale well, incurs round-trip delay, and requires constant network connectivity. Many also require users to manually confirm the shortlisted matched landmarks, which is inconvenient, slow, and prone to selection error. To overcome these limitations, we propose a h ighly a utomated (in terms of image confirmation after taking images) i mage-based l ocalization algorithm (HAIL), distributed in mobile devices. HAIL achieves resource efficiency (in terms of storage and processing) by keeping only distinguishing visual features for each landmark, and employing the efficient k-d tree to search for features. It further utilizes motion sensors and map constraints to enhance the localization accuracy without user operation. We have implemented HAIL on Android platforms and conducted extensive experiments in a food plaza and a premium shopping mall. Experimental results show that it achieves much higher localization accuracy (reducing the localization error by more than 20%) and computation efficiency (by more than 40% in time) as compared with the state-of-the-art approaches.

Economic unit commitment is a mix-integer large scale optimization problem calling for powerful and efficient tools. On the other hand, environmental impact related to the power generation is attracting increasing attentions due to the global warming trend and urgent calls for sustainable energy development. In this paper, the dual objectives of economic and emission unit commitment is converted into a single objective problem. For solving this, a novel binary Jaya optimization is proposed and integrated with lambda iteration method. The proposed binary Jaya method is inspired by the Jaya evolution and generates binary bits from a v-shape transfer function. Numerical study demonstrates the significant improvement of the binary Jaya in regarding the convergence speed for solving unit commitment problem. The solution distributions of the both objectives also show the effective of the proposed methods.

Wireless signals have been a strong indicator of nearby wireless environment, which can be used in a wide range of applications, including the construction and maintenance of wireless network in smart cities. However, wireless signals can change drastically due to moving pedestrians and automatic power adjustment of Access Points (APs), which renders previous radiomap inaccurate. To achieve sufficient accuracy, surveyors have to update the radiomap constantly, which incurs high maintenance cost in the long run. To address this, we propose RecNet, a neural network to reconstruct a fine-grained radiomap with a small number of new samples. The intuition lies in the visualization of numerical signal strength values by a heatmap. A high-resolution heatmap corresponds to a fine-grained radiomap while a low-resolution one corresponds to a coarse-grained radiomap. Then we reduce the radiomap reconstruction to the image super-resolution: generating a high-resolution image from a low-resolution one. Based on the above, we design and implement the RecNet based on the image super-resolution neural network. Extensive experiments in two large test sites demonstrate that RecNet is able to reconstruct an accurate radiomap with only 50% of fingerprints, and reduces the signal error by more than 20% compared with a recent reconstruction algorithm.

Electric vehicles provide an opportunity to reduce fossil fuel consumptions and to decrease the emissions of green-house gas and air pollutants from the transport sector. The adoption of a large number of plug-in electric vehicles however imposes significant impacts on the power system operation due to uncertain charging and discharging patterns. In this paper, multiple charging and discharging scenarios of electric vehicles together with the grid integration of renewable energy sources are examined and evaluated within the unit commitment problem. A quantum-inspired binary particle swarm optimization method is employed to determine the on/off status of each unit. Comparative studies show that the off-peak charging and peak discharging scenario is a viable option to significantly reduce the economic cost and to complement the renewable energy generation.

Dynamic economic load dispatch (DELD) is one of the most important steps in power system operation. Various optimisation algorithms for solving the problem have been developed; however, due to the non-convex characteristics and large dimensionality of the problem, it is necessary to explore new methods to further improve the dispatch results and minimise the costs. This article proposes a hybrid differential evolution (DE) algorithm, namely clonal selection-based differential evolution (CSDE), to solve the problem. CSDE is an artificial intelligence technique that can be applied to complex optimisation problems which are for example nonlinear, large scale, non-convex and discontinuous. This hybrid algorithm combines the clonal selection algorithm (CSA) as the local search technique to update the best individual in the population, which enhances the diversity of the solutions and prevents premature convergence in DE. Furthermore, we investigate four mutation operations which are used in CSA as the hyper-mutation operations. Finally, an efficient solution repair method is designed for DELD to satisfy the complicated equality and inequality constraints of the power system to guarantee the feasibility of the solutions. Two benchmark power systems are used to evaluate the performance of the proposed method. The experimental results show that the proposed CSDE/best/1 approach significantly outperforms nine other variants of CSDE and DE, as well as most other published methods, in terms of the quality of the solution and the convergence characteristics.

The Takagi–Sugeno–Kang-type rule-based fuzzy model has found many applications in different fields; a major challenge is, however, to build a compact model with optimized model parameters which leads to satisfactory model performance. To produce a compact model, most existing approaches mainly focus on selecting an appropriate number of fuzzy rules. In contrast, this paper considers not only the selection of fuzzy rules but also the structure of each rule premise and consequent, leading to the development of a novel compact rule-based fuzzy model. Here, each fuzzy rule is associated with two sets of input attributes, in which the first is used for constructing the rule premise and the other is employed in the rule consequent. A new hybrid learning method combining the modified harmony search method with a fast recursive algorithm is hereby proposed to determine the structure and the parameters for the rule premises and consequents. This is a hard mixed-integer nonlinear optimization problem, and the proposed hybrid method solves the problem by employing an embedded framework, leading to a significantly reduced number of model parameters and a small number of fuzzy rules with each being as simple as possible. Results from three examples are presented to demonstrate the compactness (in terms of the number of model parameters and the number of rules) and the performance of the fuzzy models obtained by the proposed hybrid learning method, in comparison with other techniques from the literature.