Short-Term Urban Water Demand Prediction Considering Weather Factors

Accurate and reliable forecasting plays a key role in the planning and designing of municipal water supply infrastructures. Recent studies related to water demand prediction have shown that water demand is driven by weather variables, but the results do not clearly show to what extent. The principal aim of this research was to better understand the effects of weather variables on water demand. Additionally, it aimed to offer an appropriate and reliable technique to predict municipal water demand by using the Gravitational Search Algorithm (GSA) and Backtracking Search Algorithm (BSA) with Artificial Neural Network (ANN). Moreover, eight weather factors were adopted to evaluate their impact on the water demand. The principal findings of this research are that the hybrid GSA-ANN (Agent?=?40) model is superior in terms of fitness function (based on RMSE) for yearly and seasonal phases. In addition, it is evidently clear from the findings that the GSA-ANN model has the ability to simulate both seasonal and yearly patterns for daily data water consumption.     

Training size optimization with reduced complexity in cell-free massive MIMO system

Training sequence is used in multiple antenna systems to estimate channel state information and mitigate channel distortion between transmitter and receiver. However, the training sequence or pilot must be limited to a certain size in order to reduce the impact of overhead loss due to limited channel coherence length in mobile users. In this paper, we proposed to use training size optimization in cell-free massive MIMO system. In addition, we proposed and compared the performance of different training size optimization algorithms, namely exhaustive search optimization, bisection optimization and min–max optimization, with each method has different level of calculation complexities. The results showed that in general, all of the 3 training length optimization methods improved the downlink rate compared to the conventional pilot length method. We also showed that the training optimization methods are more effective when the coherence length is small or the number of users is very large. In the case of large number of users or small coherence length, the exhaustive search has the best median downlink rate, followed closely by min–max optimum and finally the bisection method. Even though the exhaustive search optimization has the best downlink rate, we showed that the proposed reduce optimization complexity methods has significantly less calculation complexity. In addition, the median downlink rate performance of min–max optimization method is only slightly less than that of the exhaustive search method for various number of users and coherence length.

     

Assessment of wheelchair skills based on analysis of driving style

In this paper, we propose a learning assessment method based on the analysis of learner’s behavioural style. This method was first applied for wheel-chair driving tasks because it is simple and risk-free, but unusual for users. Seven classic performance indicators based on joystick control were used to characterise the users’ driving style. We assumed that the learning effectiveness of the users can be evaluated by comparing their driving style with the reference ones, which could be extracted from experienced users. The evaluation was carried out for six novice users and eight experienced users. The users were asked to carry out several typical driving tasks for seven trials at first. The fuzzy C-means clustering method was used with the data of the experienced users to obtain the reference driving styles. Next, an evaluation was performed for novice users by comparing their driving styles with the reference ones. The results showed that, for all of the experienced users, their driving styles could be classified into two reference types. In addition, there was no significant difference in their driving styles from one trial to another, even for a user with disabilities, which means that their driving style was stable. On the other hand, novice users had switching behaviours during the learning phase; however, after eight additional trials, each novice user’s driving style converged to one of the two identified reference types, meaning that the novice users could achieve a stable performance after learning, which was also validated by an expert therapist.     

Performance trade-offs in single-photon avalanche diode miniaturization

Single-photon avalanche diodes (SPADs) provide photons' time of arrival for various applications. In recent years, attempts have been made to miniaturize SPADs in order to facilitate large-array integration and in order to reduce the dead time of the device. We investigate the benefits and drawbacks of device miniaturization by characterizing a new fast SPAD in a commercial 0.18 microm complementary metal oxide semiconductor technology. The device employs a novel and efficient guard ring, resulting in a high fill factor. Thanks to its small size, the dead time is only 5 ns, resulting in the fastest reported SPAD to date. However, the short dead time is accompanied by a high after-pulsing rate, which we show to be a limiting parameter for SPAD miniaturization. We describe a new and compact active-recharge scheme which improves signal-to-noise tenfold compared with the passive configuration, using a fraction of the area of state-of-the-art active-recharge circuits, and without increasing the dead time. The performance of compact SPADs stands to benefit such applications as high-resolution fluorescence-lifetime imaging, active-illumination three-dimensional imagers, and quantum key distribution systems.     

ROMP‐based boron nitride composites

The present work focuses on the investigation of the thermal and dielectric properties of composites obtained by surface‐modified hexagonal boron nitride (hBN) and ring‐opening metathesis polymerization (ROMP) based polymer. A new kind of high performance composites was developed based on using amino silane functionalized hBN (AS‐hBN) and bromine functional group possessing homo and copolymers synthesized via ROMP pathway. Aminosilane capped boron nitride (BN) and bromine bearing polymer backbone enhance the interaction between filler and the polymer chains. The effects of surface‐modified BN (AS‐hBN) and its content on the dielectric properties, and thermal resistance of composites, are systematically investigated and discussed. The resultant composites possess high electrical break over voltages. While all of the ROMP‐based films exhibit low ?′ value in a wide frequency range, in the case of the composite with 20% AS‐hBN and poly(bromooxanorbornene‐co‐cyclooctadiene) (ROMP‐BN‐6) displays very low dielectric constants in around 1.5 up to 1 MHz at 20 °C. This value is significantly lower than that of common polymer dielectrics, which is usually in the range of 3–6. Besides the lowest dielectric constant of ROMP‐BN‐6, it has also the smallest dielectric loss tangent even at high temperatures. Tan δ of ROMP‐BN‐6 is 0.003 and 0.0067 at 10 Hz–1 MHz at 20 °C, respectively. Thermal stability of polymers was also improved by introducing surface‐modified hBN. Polymers bearing 20% AS‐hBN are highly thermally stable up to ～350 °C and gave 25% char yield at 800 °C. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 45658.     

Application of Soft Actuation to Bilateral Control and Haptic Reproduction

International Journal of Control, Automation and Systems - The main research topic of this paper is to apply the sliding mode based soft actuation to smooth transition between position, force, and...     

Pulmonary Diffuse Airspace Opacities Diagnosis from Chest X-Ray Images Using Deep Convolutional Neural Networks Fine-Tuned by Whale Optimizer

Wireless Personal Communications - The early diagnosis and the accurate separation of COVID-19 from non-COVID-19 cases based on pulmonary diffuse airspace opacities is one of the challenges facing...