Robots with telepresence capabilities are typically employed for tasks where human presence is not feasible due to geography, safety risks like fire or radiation exposure, or other factors like any epidemic disease. Time delay is a significant consideration in controlling a telepresence robot. This study proposes a deep learning-based approach to compensate for the delay by predicting the behaviour of the teleoperator. We integrate a recurrent neural network (RNN) based on the Long Short-Term Memory (LSTM) architecture with the reinforcement learning-based Deep Deterministic Policy Gradient (DDPG) algorithm. The proposed method predicts the teleoperator’s angular and linear controlling commands by using data gathered by embedded sensors on the specially designed and built telepresence robot. Simulations and experiments assess the operation of the proposed technique in Gazebo simulation and MATLAB with ROS integration, which shows 2.3% better response in the presence of static and dynamic obstacles.

Synthetic aperture sonar image reconstruction relies on the coherence of overlapping phase centers to provide accurate micronavigation for a sensed scene. It is shown that phase centers lose coherence for near-range scattering from large SAS arrays due to the fundamentally bistatic nature of these sensors. This effect is modeled using the van Cittert-Zernike theorem and a point-based sonar scattering model. Reduction of the window length used in the delay estimation process can partially mitigate the loss of coherence at the expense of increased variance in the resulting delay estimates.

Today, the elderly population is increasing and they endure many drawbacks, especially defects in their knee joints which lead to improper gait. To solve this problem, their knee joint can be replaced with knee arthroplasty. In this letter, the level of improvement in the human gait before and after total knee arthroplasty (TKA) surgery is investigated using the Dynamic Time Wrapping (DTW) algorithm. For this purpose, several volunteers who have problems with their knees incorporate in a test before and after TKA surgery, and data of gait analysis is collected. Then, the obtained data is compared with a reference using the DTW algorithm. The outcome results illustrate an improvement of 89\% - 97\% by the proposed algorithm after TKA surgery. Therefore, patients can see improvement with high speed and accuracy that result in more promising for using TKA surgery.

A dual-polarized symmetrically cross-slotted square patch (SCSSP) antenna with multimode resonance is proposed for 5G millimeter-wave broadband applications. A symmetrical cross-slot is etched on the square patch surface to change the original E-field distribution where a whole square patch is cut into four disconnected equal parts. For both polarizations, this etching also tunes the resonance frequencies of the two modes to be sufficiently close to each other. Therefore, the antenna can realize good impedance performance within the wide designated bandwidth. Dual-polarized radiation of the SCSSP is vertically excited by three bow-tie-shaped slots and fed by two orthogonal substrate integrated coaxial lines. Simulated and measured results show that the fabricated prototype achieves a broad overlapped impedance bandwidth of 50.0% (24-40 GHz), isolation higher than 30 dB between the two input ports, stable radiation pattern, and low cross-polarization over the operating band. Moreover, the proposed SCSSP antenna with compact size, planar shape, and simple vertical feeding is very well-suited for two-dimensional array design.

High level of fault currents has become one of the most challenging issues, along with increased electrical energy consumption and power grid expansion. However, some of the previous work was limited to installing limiters on designs and installing them on small plans. In this study, the optimal placement of a resistive-type superconducting fault current limiter (RSFCL) to achieve good power system transient stability is proposed. The proposed method has several advantages. First, procedure fault analysis is applied to consider the magnitude of the short-circuit currents and the line located close to the generator for the placement of the device. Compared with other methods, it can directly find the exact line to be installed RSFCL. Second, the stability assessment is validated using the time domain simulation by installing limiters with varying capacities in any location. Third, parameters were analysed to evaluate the proposed method’s effectiveness, including angular speed, rotor angular velocity, and generator rotor angle in a multi-machine system. The result reveals that the placement of the limiter on the line with the most significant short-circuits value on the line closest to the generator increases the power system stability more than the placement in other locations.

The tire lateral force control is crucial to vehicle lateral stability. Vehicle side slip and out of control can be prevented effectively by observing accurately the lateral force. Thus, a novel hyperbolic tangent sliding mode observation algorithm (NTSMO) is proposed. The algorithm adopts the longitudinal tire force error as feedback considering vehicle parameter uncertainties and without a complex tire model. First, the on-line verification of the algorithm was carried out by dSPACE to using the experimental data of the real vehicle linear acceleration and deceleration conditions, and comparison of experimental output with different observation algorithms. Furtherly, the simulation under emergency obstacle avoidance conditions and the double-line shifting conditions were conducted to verify the accuracy of the algorithm respectively. Simulation results show that the percentage errors between the tire lateral forces from the proposed NTSMO and the actual data are less than 5.35%, and the prediction accuracy of the NTSMO by 38.78% is higher than that of the sliding mode observation(SMO), which indicates that the NTSMO is superior to the SMO.

Power systems around the world are rapidly transitioning to much higher shares of inverter-based resources (IBRs) with few synchronous generators remaining online. IBRs and synchronous generators have fundamentally different dynamic performance characteristics resulting in a difference in the overall power system dynamic performance. IBRs are generally more flexible and controllable than synchronous generators, however at the same time exhibit significantly more complex control systems. Furthermore, new and emerging capabilities are being developed progressively and in particular the so-called grid-forming inverters. Grid-forming inverters (GFM) offer several new capabilities not previously possible with conventional grid-following inverters (GFL). However, they are not well understood currently when applied in a mega scale and moving forward when they will likely take over the role synchronous generators have been performing for several decades as the workhorse of system security support. Key questions currently in the technical community include the extent to which GFM shall be similar or different to each of the synchronous machines and conventional GFL, and how various control strategies can assist in maximising the grid support capabilities sought and minimise or ideally eliminate any adverse impacts. The objective of this special issue is to provide insights into some of these unknowns.

Energy routers based on the electronic power transformer are suitable for the AC-DC hybrid grid with multiple voltage levels, but their structures are complex. This paper proposes a novel energy router based on the multi-winding line frequency transformer. By a combination of a multi-winding line frequency transformer and power electronic devices, the proposed energy router can take advantage of the high reliability of the multi-winding line frequency transformer and the high controllability of power electronic devices. The proposed energy router is suitable for the AC-DC hybrid grid with multiple voltage levels and has the characteristic of a simple structure. The simulations and experimental results demonstrate the effectiveness of the proposed energy router.

Facial expression has been widely used in clinical practice to assess pain in newborns. However, the inherent visual attention required to make such vital inference is poorly understood. It is also unknown whether this inference occurs differently when comparing health professionals with other adults. To investigate these issues, we have recorded and monitored the pupil size signal of 102 subjects (44 experts, 29 parents, and 29 non-experts) while visually analyzing 20 frontal face images of 10 distinct newborns after a painful procedure and at painless rest. Our experimental results have showed that neonatal pain assessment is more cognitively demanding when analyzing the presence of pain rather than its absence. Moreover, our results disclose that a 2-second exposure to a facial expression is sufficient to make this assessment, regardless whether done by health professionals or non-health ones, suggesting that this highly specific visual task is not driven by clinical experience.

The indirect benefits of battery energy storage system (BESS) on the generation side participating in auxiliary service are hardly quantified in prior works. Nevertheless, the configuration of BESS could be affected by its indirect benefits. In this paper, we purpose a quantitative economic evaluation method of BESS considering the indirect benefits from the reduction in unit loss and the delay in investment. First, we complete further the cost model of BESS for frequency and peak regulation based on the whole life cycle theory. Second, we quantify the indirect benefits of BESS in thermal power plants based on the theory of rotor fatigue life loss and establish a benefits model that considers the unit loss reduction during frequency regulation and the delay in investment during peak regulation. Finally, we propose a set of indexes for economic evaluation of the thermal power plant with BESS. The simulation results show that the total benefits of BESS can be improved effectively by considering the indirect benefits from unit loss reduction and the delay in investment, proving the effectiveness of the proposed approach which can be meaningful for the future investment in BESS on the generation side.

Interrupted sampling repeater jamming (ISRJ) is a novel intra-pulse coherent jamming based on digital radio frequency memory (DRFM). By repeatedly sampling and retransmitting the radar transmitting signal fragments, a series of false targets can be formed after pulse compression (PC), posing a severe threat to modern radar systems. Inspired by the energy function method and histogram analysis in image processing, an adaptive time-frequency (TF) filtering method is proposed in this letter. The ISRJ-contaminated regions can be accurately determined in the TF image after histogram analysis and subsequent energy accumulation. Guided by the energy function, the proposed method can automatically adjust the intensity threshold in TF image histogram analysis and therefore reveals better robustness compared with other competing methods. Simulations have verified the effectiveness and robustness of the proposed method against ISRJ under various circumstances.

A high speed and low power input/output buffer for time interleaving circuit is proposed in this letter. The buffer can be applied to high speed circuits operating at 20GS/s. This novel two-stage buffer is employed with bandwidth expansion and slew-rate enhanced techniques. An improved common-mode feedback circuit stabilizes the output common-mode voltage. This prototype buffer is fabricated in 45nm COMS process, and achieves 7.2bit ENOB at 10GHz input frequency with power consumption of 20.4mW, load of 0.3fF.

In this study, an optimisation model is developed for two-stage energy management of a residential building to minimise energy cost under monthly power-based tariffs for peak demand and time variable electricity prices. The expected peak demand is determined in the first stage, and then the energy management system minimises the energy cost in the second stage. The optimisation problem of the second stage is solved in a rolling time window for the real-time operation of the flexible energy sources in the building. The optimal charging and discharging of the battery energy system, the charging of the electric vehicle battery, the operation of the heating system and the optimal start times of washing machines and dishwashers are determined close to real-time. The proposed approach allows the user to determine the expected peak during the month ahead and try to keep the peak demand in daily operation below that value using a close to real-time energy management system. The performance of the two-stage approach for demand-side management of a residential building has been validated by a realistic case study.

Recently, power interruptions with high impact effects are increased by occurring natural disasters. Furthermore, the uncertain nature of data creates the considerable challenges for enhancing the resiliency of power distribution systems after occurring events. Regarding this, this paper presents a robust optimization approach for resiliency improvement in power distribution system. The robust approach uses the crew teams for switching action as restoration process, demand response programs and Mobile Generators (MGs) simultaneously for improving the resiliency with considering the uncertainty of electrical load and electrical price. The objective function is tri-level consists of minimum, maximum and minimum function. The first level is minimum function that is for minimizing cost of commitment of CHPs with considering location of MGs and reconfiguration structure in power distribution systems. The second level is maximum function that is for finding the worst-case scenario of the uncertainty variables. The third level is minimizing total operation cost in the condition with worst scenario of stochastic data by using demand response programs. The aforementioned algorithm is implemented on an IEEE 33-bus test distribution system with four different cases. Furthermore, several cases and sensitivity analysis are accomplished in order to show the efficiency of the proposed model.

A novel microwave limiter with non-reciprocal limiting threshold is proposed in this paper to protect the transceiver switch or the transmitter. The directivity of the directional coupler is utilized to make the power of the received signal input to the detection circuit larger than that of the transmitted signal, thereby the detection circuit provides different DC bias voltage to the limiter circuit and changes the threshold level of the limiter diode. The test results show that this limiter has a threshold level of 35 dBm for the transmitted signal and 17 dBm for the received signal, which has a non-reciprocal limiting threshold for high-power signals input in both directions.

Drug treatment in elderly patients is a challenge for healthcare systems. This challenge is exacerbated by age-related changes in pharmacokinetics and pharmacodynamics, multimorbidity, frailty and cognitive impairment. Moreover, old patients are commonly exposed to polypharmacy, leading to increased risk of drug interactions, adverse drug reactions, and higher costs for the healthcare systems. Thus, the complex task of prescribing medications to older polymedicated patients encourages the use of Clinical Decision Support Systems (CDSS). This paper evaluates the CDSS miniQ for identifying potentially inappropriate prescribing in older adults with polypharmacy and to assesses the usability and acceptability of miniQ in health care professionals, patients, and caregivers. It describes how useful is CDSS miniQ system for Primary Care physicians, as well as for patients and their caregivers to know their medications. The discussion emphasizes how this system is useful to improve the prescribing process and reduce errors.

Integration of Distributed Energy Resources (DERs) in power systems exacerbates the existing information problems between power utilities and regulators. DER policies oblivious to the trilemma of information asymmetry between power utilities, DER aggregators, and regulators result in distorted price signals to DER investors, and socially inefficient DER roll-out. Therefore, in this paper, we propose a game-theoretic approach for modeling information asymmetry in distribution network information and consumer data between the DER aggregators and the power utilities. The proposed framework is based on Single Leader Single Follower (SLSF) games, reformulated as Mathematical Programs with Equilibrium Constraints (MPECs), and solved using the Scholtes’s relaxation technique. Our results, based on the 7-bus Manhattan power network, show that unless the DER aggregators have complete information about the distribution network characteristics, the welfare along with the realized installed capacity of DERs in the system decreases. Moreover, progressively decreasing DER investment costs alleviate the effects of information asymmetry, suggesting that early adopters face disproportionately high welfare losses attributed to incomplete information between the DER stakeholders. Hence, policy interventions to alleviate the rampant information problems are imperative to ensure an optimal DER roll-out.

Deep learning-based classification algorithms have been used for automatic modulation recognition (AMR). However, most methods only focus on end-to-end mapping and neglect the classic key features. In this paper, signals are enforced with key classification features to propose a novel deep learning model for AMR by learning the shared latent space of the aligned signals and key features (LLAF); this is done to increase the generalizability of the model and to ensure the physical plausibility of the results. To obtain adequate signal representations, an encoder-decoder architecture is proposed to learn the shared latent space, and the architecture is trained to approximate prior label distributions for precise signal classification. Simulation results verify the high recognition accuracy of the proposed LLAF model under different signal-to-noise ratios (SNRs).