Vibration energy harvesting is a promising solution for powering battery-free IoT systems; however, the instability of ambient vibrations presents significant challenges, such as limited harvested energy, intermittent power supply, and poor adaptability to various applications. To address these challenges, this paper proposes ViPSN2.0, a modular and reconfigurable IoT platform that supports multiple vibration energy harvesters (piezoelectric, electromagnetic, and triboelectric) and accommodates sensing tasks with varying application requirements through standardized hot-swappable interfaces. ViPSN 2.0 incorporates an energyindication power management framework tailored to various application demands, including light-duty discrete sampling, heavyduty high-power sensing, and complex-duty streaming tasks, thereby effectively managing fluctuating energy availability. The platform’s versatility and robustness are validated through three representative applications: ViPSN-Beacon, using an ultra-lowcost structural PZT (ϕ35 mm, <0.002 $) to enable a BLE advertisement from a single transient fingertip press with 100 m line of sight; ViPSN-LoRa, supporting wireless communication powered by wave vibrations in actual marine environments (Bohai Bay) with per-uplink task energy compatible with kilometer-scale field links; and ViPSN-Cam, enabling intermittent image capture and wireless transfer, delivering one frame approximately every 15 s under typical conditions. Experimental results demonstrate that ViPSN 2.0 can reliably meet a wide range of requirements in practical battery-free IoT deployments under energy-constrained conditions.

Wheel flats (WF) are a common defect in railway systems, posing risks to operational safety, passenger comfort, and the longevity of infrastructure. Existing detection methods face significant challenges, including sparse labeled data, high noise interference, and limited adaptability to complex operational conditions. To address these issues, this study introduces a semi-supervised learning workflow integrating multi-sensor data from Distributed Acoustic Sensing (DAS) and accelerometers, with a novel Graph Vector-Quantization Variational AutoEncoder (GVQVAE) as the core component. The model combines time-frequency analysis for feature extraction, a graph-based architecture for data fusion, and a vector quantization mechanism to effectively leverage both labeled and unlabeled data. Experimental results from an operational subway system demonstrate the model’s robustness and high accuracy, with an average detection accuracy of 97.08%. These findings highlight the potential of the proposed DAS-accelerometer fusion and GVQVAE model as an effective, scalable solution for enhancing WF detection in modern railway systems.

Monitoring the health of vehicle-track system using deep learning and distributed fiber optic sensing presents a significant challenge due to the vast volume of real-time data and the difficulty of directly assessing the system’s condition. This often results in a severe imbalance in the distribution of extreme samples within the dataset, as large-scale signal collection typically lacks manual labeling. Consequently, supervised deep learning models face limitations due to insufficient labeled training data, while unsupervised deep learning models struggle with contamination from ambiguous samples whose health status remains unclear, hindering the development of robust and accurate models. To address this challenge, we propose SemAnoDiffusion, a semi-supervised model based on blur diffusion and an enhanced contrastive loss training approach. SemAnoDiffusion leverages a small set of labeled data alongside a large amount of unlabeled samples to accurately differentiate between anomalous data, normal data, and ambiguous samples that fall between these categories. In a case study of a metro system in Singapore, Distributed Acoustic Sensing and accelerometer arrays were used to collect track vibration responses as trains passed, with wheel flats occurring in a small subset of the trains. SemAnoDiffusion achieved 100% accuracy in classifying manually labeled normal and anomalous samples and effectively identified semi-damaged samples with unclear damage levels from the labeled data, successfully detecting all trains with wheel flats.

Being able to see into walls is crucial for diagnostics of building health; it enables inspections of wall structure without undermining the structural integrity. However, existing sensing devices do not seem to offer a full capability in mapping the in-wall structure while identifying their status (e.g., seepage and corrosion). In this paper, we design and implement SiWa as a low-cost and portable system for wall inspections. Built upon a customized IR-UWB radar, SiWa scans a wall as a user swipes its probe along the wall surface; it then analyzes the reflected signals to synthesize an image and also to identify the material status. Although conventional schemes exist to handle these problems individually, they require troublesome calibrations that largely prevent them from practical adoptions. To this end, we equip SiWa with a deep learning pipeline to parse the rich sensory data. With innovative construction and training, the deep learning modules perform structural imaging and the subsequent analysis on material status, without the need for repetitive parameter tuning and calibrations. We build SiWa as a prototype and evaluate its performance via extensive experiments and field studies; results evidently confirm that SiWa accurately maps in-wall structures, identifies their materials, and detects possible defects, suggesting a promising solution for diagnosing building health with minimal effort and cost.

Whereas a lot of efforts have been put on energy conservation in wireless sensor networks (WSNs), the limited lifetime of these systems still hampers their practical deployments. This situation is further exacerbated indoors, as conventional energy harvesting (e.g., solar) may not always work. To enable long-lived indoor sensing, we report in this article a self-sustaining sensing system that draws energy from indoor environments, adapts its duty-cycle to the harvested energy, and pays back the environment by enhancing the awareness of the indoor microclimate through an “energy-free” sensing. First of all, given the pervasive operation of heating, ventilation, and air conditioning (HVAC) systems indoors, our system harvests energy from airflow introduced by the HVAC systems to power each sensor node. Secondly, as the harvested power is tiny, an extremely low but synchronous duty-cycle has to be applied whereas the system gets no energy surplus to support existing synchronization schemes. So, we design two complementary synchronization schemes that cost virtually no energy. Finally, we exploit the feature of our harvester to sense the airflow speed in an energy-free manner. To our knowledge, this is the first indoor wireless sensing system that encapsulates energy harvesting, network operating, and sensing all together.

For indoor Wireless Sensor Networks (WSNs), as the conventional energy harvesting (e.g., solar) ceases to work in an indoor environment, the limited lifetime is still a threaten for practical deployment. We report in this demo a self-sustaining indoor sensing system. First of all, given the pervasive operation of heating, ventilation and air conditioning (HVAC) systems indoors, our system harvests energy from airflow introduced by the HVAC systems to power each sensor node. Secondly, as the harvested power is tiny (only of hundreds of μW) such that the exiting sensor products cannot be afforded due to their high energy consumption, we exploit the feature of our harvester to sense the airflow speed in an energy-free manner, which can pay back the environment by enhancing the awareness of the indoor microclimate. We also present two complementary algorithms to synchronize the duty-cycles of the sensor nodes to adapt to the energy harvesting. To our knowledge, this is the first indoor wireless sensing system that encapsulates energy harvesting, network operating, and sensing all together.

Whereas a lot of efforts have been put on energy conservation in wireless sensor networks, the limited lifetime of these systems still hampers their practical deployments. This situation is further exacerbated indoors, as conventional energy harvesting (e.g., solar) ceases to work. To enable long-lived indoor sensing, we report in this paper a self-sustaining sensing system that draws energy from indoor environments, adapts its duty-cycle to the harvested energy, and pays back the environment by enhancing the awareness of the indoor microclimate through an "energy-free" sensing.

Genetic algorithms (GA) do have some advantages over gradient-based methods for solving topology design optimization problems. However, their success depends largely on the geometric representation used. In this work, an enhanced morphological representation of geometry is applied and evaluated to be efficient and effective in producing good results via a target matching problem: a simulated topology and shape design optimization problem where a dasiatargetpsila geometry set is first predefined as the Pareto optimal solutions and a multiobjective optimization problem formulated such that the design solutions will evolve and converge towards the target geometry set. As the objectives (and constraints) are conflicting, the problem is challenging and an adaptive constraint strategy is also incorporated in the GA to improve convergence towards the true Pareto front.

This paper describes a technique for design under uncertainty based on hybrid genetic algorithm. In this work, the proposed hybrid algorithm integrates a simple local search strategy with a constrained multi-objective evolutionary algorithm. The local search is integrated as the worst-case-scenario technique of anti-optimization. When anti-optimization is integrated with structural optimization, a nested optimization problem is created, which can be very expensive to solve. The paper demonstrates the use of a technique alternating between optimization (general genetic algorithm) and anti-optimization (local search) which alleviates the computational burden. The method is applied to the optimization of a simply supported structure, to the optimization of a simple problem with conflicting objective functions. The results obtained indicate that the approach can produce good results at reasonable computational costs.

This paper demonstrates the automatic design of a compliant grip-and-move manipulator by topology and shape optimization using a hybrid genetic algorithm. It presents the novel idea of integrating both grip and move behaviors within one simple compliant actuator mechanism. The manipulator employs two identical path generating compliant mechanisms with two degrees-of-freedom each so that it can grip a workpiece and move it to anywhere within its working area. They are difficult to design because their motions have to be analyzed by finite element methods and the relationship between their geometry and their elastic behavior is highly complex and non-linear. The synthesis of such a mechanism in this work is achieved by a structural optimization approach based on a hybrid genetic algorithm and a morphological representation scheme for defining the structural geometry upon a finite element grid. The proposed hybrid algorithm integrates a simple local search strategy with a constrained multiobjective evolutionary algorithm. A novel constrained tournament selection is used as a single objective function in the local search strategy. The selection is utilized to determine whether a new solution generated in local search process will survive. Hooke and Jeeves method is applied to decide search path. Good initial solutions, the solutions to be mutated, are chosen for local search.

This paper describes a technique for design under uncertainty based on hybrid genetic algorithm. In this work, the proposed hybrid algorithm integrates a simple local search strategy with a constrained multi-objective evolutionary algorithm. The local search is integrated as the worst-case-scenario technique of anti-optimization. When anti-optimization is integrated with structural optimization, a nested optimization problem is created, which can be very expensive to solve. The paper demonstrates the use of a technique alternating between optimization (general genetic algorithm) and anti-optimization (local search) which alleviates the computational burden. The method is applied to the optimization of a simply supported structure under load uncertainties, to the optimization of a simple problem with conflicting objective functions. The results obtained indicate that the approach can produce good results at reasonable computational costs.

Electromechanical (EM) impedance method for structural health monitoring (SHM) is based on detecting the changes of the measured signatures of the Lead Zirconate Titanate (PZT) EM admittance (the inverse of the impedance). Although this method has been successfully applied for various engineering structures for damage detection, it is unable to specify the effect of damage on structural properties. The direct indicator of the structural properties is the structural mechanical impedance which can be extracted from the PZT EM admittance signatures. To model the structural impedance, this paper presents a multiple-degrees-of-freedom system consisting of a number of one-degree-of-freedom elements with mass, spring and damper components. Genetic algorithms (GAs) are employed to search for the optimal solution of the unknown dynamic system parameters by minimizing an objective function. Experiment has been carried on a two-storey concrete frame subjected to base vibrations that simulate earthquake. A number of PZT transducers are regularly arrayed and bonded to the frame structure to acquire PZT EM admittance signatures. The changes of the structural parameters in the model system are quantified using GAs. The relation between the distance of the PZT transducer away from the damage and the changes of the structural parameters identified by the PZT transducer is studied. Finally, the sensitivity of the PZT transducers is discussed.