This paper presents the formulation, design procedure, and application of a hybrid model predictive control (HMPC) scheme for hybrid systems that is embedded in a mixed logical dynamical (MLD) framework. The proposed approach adopts a three degrees-of-freedom (3DoF) tuning method to accomplish precise setpoint tracking and ensure robustness in the face of disturbances (both measured and unmeasured) and uncertainty. Furthermore, the algorithm employs setpoint and disturbance anticipation to proactively enhance controller performance and potentially reduce control effort. Slack variables in the objective function prevent the mixed-integer quadratic problem from becoming infeasible. The effectiveness of the proposed algorithm is demonstrated through its application in three distinct case studies, which include control of production-inventory systems, time-varying behavioral interventions for physical activity, and management of epidemics/pandemic prevention. These case studies indicate that the HMPC algorithm can effectively manage hybrid dynamics, setpoint tracking and disturbance rejection in diverse and demanding circumstances, while tuned to perform well in the presence of nonlinearity and uncertainty.

This paper proposes PixelPrune, an approach to address two primary challenges in artificial intelligence of things (AIoT) vision systems: (1) the energy-intensive analog-to-digital converters (ADCs) required in the sensing unit for converting analog pixel arrays to digital tensors, and (2) the high data transfers between the sensing unit and computing unit. Our proposed solution involves the implementation of an in-sensor binary segmentation model on analog memristive crossbars to identify the important pixels and prune out the background information. Additionally, we propose a data transfer scheme that adaptively selects between dense and sparse data transfer formats based on the sparsity ratio measured from the segmentation mask obtained by the segmentation model. Our results demonstrate that the proposed object detection system achieves significant energy savings along with a considerable up to 95% reduction in data transfer, all while maintaining high accuracy.

This study investigates the police treatment of individuals arrested for the possession of small quantities of marijuana in Toronto. Utilizing a dataset comprising 5226 observations, we analyze the factors that influence whether an arrestee is released with a summons. The dataset includes variables such as the arrestee's race, age, sex, employment status, citizenship status, the year of the arrest, and the number of police databases in which the arrestee's name appeared. A neural network model is developed to predict the likelihood of release based on these factors. Our findings indicate significant patterns and disparities, shedding light on the influence of demographic and socioeconomic factors on police decision-making. The results underscore the potential of machine learning models in uncovering biases and guiding policy reforms in the criminal justice system.

Predictive Control optimization typically relies on exhaustive enumeration of all possible voltage vectors within the finite control set. For simpler implementation the computational burden is not a concern, but the limited control set can demand a very high sampling frequency to reach good steady state performance. Indirect Predictive Control or Modulated Predictive Control addresses this by using a modulation stage, but classic implementation depends on duty cycle calculation that can limit constraints and non linearities in the cost function. This paper introduces the Informed Binary Search, an intelligent search algorithm capable of handling any constraints within the cost function, similarly to Finite Control Set, but with the benefit of fixed switching frequency. Experimental results on a two-level VSI driving an induction motor is shown but the method can be applied to every setup of machine and inverter. The results are compared with PTC, PTC with DSVM and MPTC and the metrics show improvements in torque and flux ripple and improvements of current THD up to 30% over classic M2PC while having similar computational burden.

We introduce an algorithm for combining two sources of similar data within a loss function to use in an optimisation procedure. The technique involves training model parameters on a ratio of two data sources to encourage convergence on meta-model parameters that map behaviours learned from each data source. The model can achieve higher quality results when unknown error distorts the signal of either source. Using a differentiable loss function we use gradient descent to optimise the hyperparameter which controls the ratio of data signals while training the model weights. We derive the theoretical bounds of the loss function based on the potential error of the meta-model, and the loss function is numerically interpreted to show the solution space. To utilise the methodology, we show its use in an example optimisation algorithm. Using this algorithm, we conducted an experimental case study to train functionapproximating surrogate models of the simple diffusion equation and the nonlinear Schrödinger equation. Thus, we show the ratio-coupled loss function may be utilised in approximating linear and nonlinear partial differential equations.

The state of power (SOP) refers to the maximum available power from a battery. The SOP is a function of the open circuit voltage and internal resistance of the battery, both of which change with the state of charge (SOC). As such, the SOP is a more informative metric of battery state than the SOC and is crucial for the continued operation of an electric vehicle. The existing approaches employ non-linear system identification techniques that are sensitive to changes along temperature and SOC. In this paper, a novel approach is proposed to estimate the state of power in real time. The proposed approach does not require non-linear approaches for both system identification and filtering. Instead, a linear least squares estimation technique is utilized to estimate the parameters needed for SOP computation. The proposed approach utilizes a mechanism to account for the change in OCV in the observation window and employs a simple yet robust technique to account for the relaxation effect of the battery. The SOP estimates computed through the proposed approach are tested using realistic battery data collected from three battery cells at the following eight different temperatures: −25◦C, −15◦C, −5 ◦C, 5 ◦C, 15◦C, 25◦C, 35◦C, and 45◦C.

The significance of maritime transportation highlights the need to enhance the efficiency of container terminals. This study addresses a challenge within maritime transportation, specifically the continuous berth allocation and time-variant quay crane assignment problem (C/T-V BACAP). We formulate a comprehensive mathematical model of C/T-V BACAP. To solve the problem, we propose an effective memetic algorithm with a heuristic decoding method. The proposed algorithm consists of three essential components: a three-stage heuristic decoding method, a clustering-based evolutionary method, and a targetguided local search strategy. The three-stage heuristic decoding method guarantees solution feasibility and high quality through the entire optimization, allowing the following strategies to fully utilize their search capabilities. The clustering-based evolutionary method refines the search space and and diversifies the promising candidates. Meanwhile, the target-guided local search strategy rapidly optimizes the allocation for the challenging vessel. The experimental results demonstrate that the proposed algorithm delivers excellent performance, especially in handling large-scale instances (up to 60 vessels). Our proposed method outperforms the state-of-the-art BACAP algorithms by an average margin of 150% in terms of berth offset and waiting time in most problem instances.

This paper explores the synergistic use of offshore wind, wave energy, and photovoltaics for maritime hydrogen refueling stations, focusing on their impact on storage requirements. Two Mediterranean locations, eastern Crete and the Galite islands, were selected as candidate locations for installing maritime hydrogen refueling stations due to their high potential in wind, wave, and solar energy. The study employs a Storage-to-Production Ratio (SPR) index, which quantifies the storage needed to maintain a consistent and uninterrupted hydrogen supply to ships year-round, relative to the total annual hydrogen production. Our findings reveal that integrating either wind or wave energy with solar power significantly decreases the SPR, attributed to the low correlation between solar and the other two energy sources. This reduction is substantial, lowering the SPR by up to 20%. Practically, assuming a refueling station with a consistent annual hydrogen supply of approximately 20.000 tons, the synergy could lead to a reduction of the hydrogen storage by 4.000 tons, i.e. 0.2×20.000 tons. On the other hand, combining wind and wave energy in Mediterranean Sea does not contribute to the SPR reduction due to substantial temporal correlation between wind and wave sources.

Lidar odometry allows for precise and reliable estimation of a vehicles movement over short durations, but suffers from drift over long trajectories. In this work, we propose an approach for reducing this drift by incorporating publicly available data from OpenStreetMap (OSM). Our approach is an extension to conventional Iterative Closest Points (ICP)-based lidar odometry with an additional registration against a map derived from the building outlines of OSM. For this, we propose a complementary filter inspired fusion step, capable of reducing the drift of lidar odometry while simultaneously avoiding the integration of erroneous map data. A detailed evaluation of our method on the KITTI odometry and Boreas data sets, as well as on our own recorded data, shows our approach to be applicable in a wide variety of urban and suburban situations, capable of reducing the drifting lidar odometry and yielding state-of-the-art results when compared to other OSM map-assisted visual odometry approaches.

Drowsy driving is a huge risk to road safety, resulting in serious accidents. Drowsiness detection in drivers can assist prevent accidents by providing timely alarms and actions. In this research, we look at how deep learning Convolutional Neural Networks (CNNs) can be used to identify tiredness. We discuss our technique, experimental results, and conclusions about the efficacy of deep learning CNNs in solving this essential road safety issue. In this work, we employed two convolutional neural networks, one is built from scratch and another one that is a pre-trained model (ResNet). Our findings show that the pre-trained outperformed the one built from scratch achieving an accuracy of 95.6%.

Vision-language models (VLMs) have demonstrated significant potential in various domains, including natural language processing and computer vision. However, their application in medical imaging, particularly in X-ray diagnosis, remains underexplored. This review article examines the current state of VLMs in the context of X-ray diagnosis, focusing on their effectiveness, challenges, and potential for expandability. We discuss various models, including Knowledge-enhanced Auto Diagnosis (KAD) and BERTHop, and their performance on different datasets. Additionally, we explore the methods used for integrating domain-specific knowledge into these models and the implications for clinical practice. The review concludes with a discussion on future directions and the potential for VLMs to revolutionize medical imaging diagnostics.

Magnetic field focusing in longitudinal direction has been a missing link for three-dimensional synthesized magnetic focusing (3-D SMF). Deep magnetic focusing (DMF) by multiple coaxial coils, a sort of 3-D SMF, is firstly proposed in this paper, where magnetic field becomes maximum at the focal range in longitudinal direction. DMF is requisite for transcranial magnetic stimulation (TMS) and wireless power transfer (WPT). As the simplest DMF, a set of differential coils with opposite current direction is implemented. Optimum coil currents, coil radii, and coil distance are designed to provide with zero magnetic field at zero range and maximum magnetic field at a focal range. This DMF performance is found to be achieved with a penalty of peak magnetic field reduction. Experiments for the differential coils of diameters 10 cm and 5 cm with 3 cm gap showed 4 cm depth DMF within 6 cm diameter. Specific numbers of turns make the DMF be driven by only a single voltage source, which is firstly adopted in SMF. The frequency versatile DMF characteristic is experimentally verified for 60 Hz and 40 kHz, where 27mW is delivered to a focal point that is a demonstration for safer WPT of implantable devices.

A new synthesized magnetic focusing (SMF) technology for multiple coaxial circular coils is proposed in this paper. The fundamental principle of the proposed coaxial plane SMF is provided comparing with previous SMF as well as transcranial magnetic stimulation (TMS) in medical applications. The proposed SMF theory determines the appropriate currents in multiple coaxial circular coils, resulting in a focused magnetic field distribution across a three-dimensional space. Simulation results show that 4.2 times sharper magnetic field with a cost of reduced peak magnetic field. Experiments for five coils with a 50 cm diameter at a focal distance of 10 cm show very good agreement with simulation. A single-source-driver is firstly used replacing with multiple drivers of conventional SMF.

In this paper, a new dynamic gyrator model is proposed for transient analysis of inductive power transfer (IPT). The well-known dynamic phasor is applied to an IPT system and then the dynamic gyrator is derived from the phasor domain circuit. The proposed dynamic gyrator has an imaginary gain similar to a static gyrator; however, it is valid not only for the steady state but also for the transient state. It is verified that IPT with two LC resonant tanks of the fourth order system is degenerated to the second order system. So far, dynamic control of transient IPT circuit was tremendously difficult due to very high order compensation circuits of IPT systems. Any IPT systems regardless of the types of compensation circuits can be analyzed by the proposed dynamic gyrator with no cumbersome equations. The degenerated low-order system with a dynamic gyrator model makes the complicated design of IPT be a substantially simple task. Simulation and experiments for a series-series compensated IPT of 520 W tuned at 50.3 kHz successfully verified the proposed dynamic gyrator model.

This paper presents a three-phase powerline energy harvesting circuit with doubly regulated output voltages to power wireless sensors for monitoring of railroad powerline status. Three ring-shaped silicon steel cores coupled to the three phases of a powerline convert the line current into three-phase voltages, which are applied to an energy harvesting circuit. The key parts of the circuit are a series three-phase voltage rectifier, a buck-boost converter operating in discontinuous conduction mode (DCM), and a microcontroller unit (MCU) for maximum power point tracking (MPPT). The MCU performs a two-step, coarse and fine, MPPT for impedance matching based on the perturb and observe method. Two parallel voltage regulators deliver 5 V and 5.7 V regulated DC voltages to power a radio and a set of sensors, respectively. The energy harvesting circuit is prototyped using commercial-off-the-shelf (COTS) components on an FR4 PCB. The measured maximum efficiency is 84% of the three-phase voltage rectifier and 89% for the buck-boost converter under the powerline current ranging from 5 A to 20 A.

This paper reports on the results of multicast latency and round trip time measurements for a particular deployment of nodes forming a Wi-SUN FAN 1.0 [1] network. The authors have explored in detail the behaviour of the WiSUN FAN network used by Hee-Jun Lee and Sang-Hwa Chung, the authors of a prior paper [2], in order to provide more detail regarding the latencies and delays cited in that paper. We provide details regarding:the timing of the component requests and responses that comprise the Lee and Chung round-trip and latency measurements, andadditional timing and packet delivery ratio results for variations of the MPL [3] retransmission parameters for the Wi-SUN FAN protocol used on the Lee and Chung test network.

Actuators play a crucial role in robotics, determining the force and speed capabilities necessary for varied tasks, directly affecting the performance of the robotic system. With the growing reliance on robotics in both industrial applications and daily life, innovative actuator research has expanded significantly. Despite advances, traditional actuators encounter limitations in performance and operational range due to inherent physical constraints. To address these challenges, variable transmission mechanisms (VTMs) have emerged over the past decade as one of the alternative solutions, enhancing the adaptability and efficiency of robotic systems. However, there is currently a lack of survey articles that comprehensively cover the mechanisms and working principles of VTMs in robotics. This review article fills this gap by offering an extensive analysis of VTM applications in robotics. It categorizes VTMs based on their mechanisms and principles, presents case studies on both commercial and experimental VTMs, and provides insights into future prospects.

The General Relativity is the currently accepted theory of Gravitation in modern physics. Finding though similarities in formulas derived from other physical laws, with the well-known Newton's universal gravitational law might give us hints on some properties and the nature of the forces and matter. This paper shows that the matter might be not as much an external entity placed inside the space-time, but instead can mostly be compressed space, and from that alone arises the gravitational force. And more, it shows that with the increasing of mass in structures like Spiral Galaxies, or Filaments of Galaxies, the gravitational constant 'G' also increases. It could in part account for the Dark Matter. And in the end, presuming the initial state of the space was compressed, might also explain the expansion of the Universe.