Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining Decision Making and Trajectory Planning for Lane Changing Using Deep Reinforcement Learning

In the context of Automated Vehicles, the Automated Lane Change system, is fundamentally based upon the separate constructs of Perception, Decision making, Trajectory Planning, and Execution. However, in existing works there are many simplistic and unplausible assumptions in applying these constructs that severely restrict their operational effectiveness in realistic and complex driving scenarios. For instance, there are rigid assumptions about the disposition of vehicles and that lane-changing maneuvers can occur instantaneously, but that highly desirable features such as the ability for real-time trajectory re-planning are lacking. In this paper, we address these limitations through an integrated methodology for lane-change decision making and trajectory planning, in which a deep Reinforcement Learning algorithm with a safe action set technique is employed in decision making that is effectively coupled to a specially devised trajectory planning model. The proposed new methodology is computationally efficient, supporting real-time implementation, and provides for lane-changing maneuvers that can be made simultaneously with other vehicles and can be dynamically re-planned; thus, enabling flexible, robust, and safe lane-changing maneuvers under the guidance of a new decision-making module. Finally, the veracity of the proposed methodology in guiding a vehicle to improve travel times and accomplish high-level driving behaviors such as overtaking and desired-speed maintenance in a range of road traffic scenarios is demonstrated in a number of numerical experiments.
Trends in Heart Rate and Heart Rate Variability During Pregnancy and the 3-Month Postpartum Period: Continuous Monitoring in a Free-living Context

Background Heart rate variability (HRV) is a noninvasive method that reflects the regulation of the autonomic nervous system. Altered HRV is associated with adverse mental or physical health complications. The autonomic nervous system also has a central role in physiological adaption during pregnancy, causing normal changes in HRV. Objective The aim of this study was to assess trends in heart rate (HR) and HRV parameters as a noninvasive method for remote maternal health monitoring during pregnancy and 3-month postpartum period. Methods A total of 58 pregnant women were monitored using an Internet of Things–based remote monitoring system during pregnancy and 3-month postpartum period. Pregnant women were asked to continuously wear Gear Sport smartwatch to monitor their HR and HRV extracted from photoplethysmogram (PPG) signals. In addition, a cross-platform mobile app was used to collect background and delivery-related information. We analyzed PPG signals collected during the night and discarded unreliable signals by applying a PPG quality assessment method to the collected signals. HR, HRV, and normalized HRV parameters were extracted from reliable signals. The normalization removed the effect of HR changes on HRV trends. Finally, we used hierarchical linear mixed models to analyze the trends of HR, HRV, and normalized HRV parameters. Results HR increased significantly during the second trimester (P<.001) and decreased significantly during the third trimester (P=.006). Time-domain HRV parameters, average normal interbeat intervals (IBIs; average normal IBIs [AVNN]), SD of normal IBIs (SDNN), root mean square of the successive difference of normal IBIs (RMSSD), normalized SDNN, and normalized RMSSD decreased significantly during the second trimester (P<.001). Then, AVNN, SDNN, RMSSD, and normalized SDNN increased significantly during the third trimester (with P=.002, P<.001, P<.001, and P<.001, respectively). Some of the frequency-domain parameters, low-frequency power (LF), high-frequency power (HF), and normalized HF, decreased significantly during the second trimester (with P<.001, P<.001, and P=.003, respectively), and HF increased significantly during the third trimester (P=.007). In the postpartum period, normalized RMSSD decreased (P=.01), and the LF to HF ratio (LF/HF) increased significantly (P=.004). Conclusions Our study indicates the physiological changes during pregnancy and the postpartum period. We showed that HR increased and HRV parameters decreased as pregnancy proceeded, and the values returned to normal after delivery. Moreover, our results show that HR started to decrease, whereas time-domain HRV parameters and HF started to increase during the third trimester. The results also indicated that age was significantly associated with HRV parameters during pregnancy and postpartum period, whereas education level was associated with HRV parameters during the third trimester. In addition, our results demonstrate the possibility of continuous HRV monitoring in everyday life settings.
Online Surface Roughness Prediction for Assembly Interfaces of Vertical Tail Integrating Tool Wear under Variable Cutting Parameters

Monitoring surface quality during machining has considerable practical significance for the performance of high-value products, particularly for their assembly interfaces. Surface roughness is the most important metric of surface quality. Currently, the research on online surface roughness prediction has several limitations. The effect of tool wear variation on surface roughness is seldom considered in machining. In addition, the deterioration trend of surface roughness and tool wear differs under variable cutting parameters. The prediction models trained under one set of cutting parameters fail when cutting parameters change. Accordingly, to timely monitor the surface quality of assembly interfaces of high-value products, this paper proposes a surface roughness prediction method that considers the tool wear variation under variable cutting parameters. In this method, a stacked autoencoder and long short-term memory network (SAE–LSTM) is designed as the fundamental surface roughness prediction model using tool wear conditions and sensor signals as inputs. The transfer learning strategy is applied to the SAE–LSTM such that the surface roughness online prediction under variable cutting parameters can be realized. Machining experiments for the assembly interface (using Ti6Al4V as material) of an aircraft’s vertical tail are conducted, and monitoring data are used to validate the proposed method. Ablation studies are implemented to evaluate the key modules of the proposed model. The experimental results show that the proposed method outperforms other models and is capable of tracking the true surface roughness with time. Specifically, the minimum values of the root mean square error and mean absolute percentage error of the prediction results after transfer learning are 0.027 μm and 1.56%, respectively.
The Impacts of Depth Camera and Multi-Sensors for Face and Fingerprint Recognition in 2D and 3D - A Case Study

This paper reviews the multi-modern sensors for multimodal (Face ad Fingerprint) of a biometric system by using a depth camera. The use of face authentication in biometric data allows this innovation to expand and be used in a variety of fields. Recently, attendance monitoring systems depending on biometric identification for higher education are underutilization, presenting an excellent chance to do fascinating experiments. The installation of biometric attendance systems necessitates the use of both hardware and software components. The first deep CNN (Convolutional Neural Network) method for light source-oriented Face Recognition (FR) takes advantage of the more detailed data given in a lens let display technology picture and that has been used in different borders such as airports, seaports, and land ports. In addition, the use of 3D camera technology for measuring medical outcomes in the healthcare marketplace is growing. The Intel® RealSense TM is one of the top 3D thermal imaging cameras systems on the market today, and it is well-suited for usage in a variety of areas such as medical systems, automation, and medical. Advances in in-depth sensor cameras technology have led to a considerable rise in the incorporation of these innovations into moveable systems, implying the great future potential for widespread in clinic and sector medical screening systems. Furthermore, initially, the use of dispersion maps in conjunction with depth maps and Two-Dimensional Red Green Blue (2D-RGB) pictures has been examined in terms of a fusion strategy to increase FR performance. The suggested approach employs the 2D-RGB crucial radiations emitted viewpoint, and depth maps and dispersion derived from the whole collection of radiations emitted pictures connected with a lens let light source. Following that, feature separation was carried out with the use of a VGG-deep sighs description for texturing and individually well their representations for depth maps and dispersion. The collected characteristics are then combined and supplied into a classification model.
Rydberg atom-based radio frequency electrometry: hyperfine effects

Hyperfine effects in Rydberg atom-based sensing have been observed for sensing polarization. However, in most work to date, the hyperfine structure involved in the radio frequency transition has been ignored because the residual Doppler widths realized in experiments are larger than the hyperfine energy splittings of the Rydberg states. Recently, we have proposed and demonstrated a collinear three photon scheme for Rydberg atom-based electrometry that has a greatly reduced residual Doppler width, < 500 kHz. In these experiments, we observe the effect of optical pumping and the hyperfine structure of the Rydberg states. We compare the 42P3/2 → 41D5/2 and 42P3/2 → 41D3/2 sensing transitions to show that Rydberg atom hyperfine structure effects can be observed at our spectral resolution. Hyperfine structure and optical pumping can alter the effective transition dipole moments on the sensing transition and can be used to detect polarization of the radio frequency field so our work is important for practical Rydberg atom electric field sensing.
Photoinitiator-free micro/nano fabrication of biomaterials with nonlinear deep UV excitation

Two-photon fabrication is expected to be a technique for fabricating biological tissues for regenerative medicine and drug discovery because of its capability of fabricating 3D structures on a subcellular scale. In this study, we conducted two-photon fabrication of biocompatible materials without photoinitiators. By using a visible-wavelength femtosecond pulsed laser as excitation light, two-photon polymerization is induced in deep UV absorbing moieties without the use of photo-initiators. We performed 3D micro/nanofabrication of a biocompatible hydrogel material. By using Raman spectral change, we investigated the photo-chemical process of the biocompatible upon the irradiation of visible pulsed laser light.
Dual-parameter amplification sensing of temperature and strain by using parallel dual Fabry–Perot interferometer

In this paper, we proposed and demonstrated a parallel optical fiber Fabry–Perot interferometer (FPI) for temperature and strain sensor based on an optimized 3×2 steps fabrication process, aiming to improve the quality of the sensitivity by use of the Vernier effect. The sensor consists of two FPIs with different propagation mediums in parallel connection, which is formed by pairs of built-in plate reflectors, another is fabricated by splicing a three-hole fiber (THF) supported by a suspension core, the higher thermal-optical coefficient can improve the temperature sensitivity. In temperature and strain sensing, two different combinations of FPIS are used as sensing units and matching reference units respectively. The amplification is matched to the wavelength range of the light source by accurately controlling the cavity length between the two FPIs. The temperature and strain sensitivities achieved in the experiment are 182.15 pm/°C and 201.47 pm/µε, respectively. The separate settings of temperature and strain sensing amplification increase the flexibility applications of dual-parameter amplification sensors.
Self-Adaptive Constrained Multi-Objective Differential Evolution Algorithm Based on the State–Action–Reward–State–Action Method

The performance of constrained multi-objective differential evolution algorithms (CMOEAs) is mainly determined by constraint handling techniques (CHTs) and their generation strategies. To realize the adaptive adjustment of CHTs and generation strategies, an adaptive constrained multi-objective differential evolution algorithm based on the state–action–reward–state–action (SARSA) approach (ACMODE) is introduced in the current study. In the proposed algorithm, the suitable CHT and the appropriate generation strategy can be automatically selected via a SARSA method. The performance of the proposed algorithm is compared with four other famous CMOEAs on five test suites. Experimental results show that the overall performance of the ACMODE is the best among all competitors, and the proposed algorithm is capable of selecting an appropriate CHT and a suitable generation strategy to solve a particular type of constrained multi-objective optimization problems.
Surface modification of silicon by femtosecond laser ablation in liquid

Pulsed laser ablation is steadily gaining popularity in micromachining to keep pace with the increasing demand for precision manufacturing and functional surfaces. However, efficient laser processing under atmospheric conditions primarily suffers from particle redeposition and therefore requires additional cleaning steps to obtain high surface quality. To reduce additional cleanings steps after manufacturing, laser ablation in liquid allows for a significant reduction in particle redeposition as particles rapidly cool down and penetrate into the liquid without stitching to the surface. However, laser ablation in liquid is accompanied by the complex interaction between the hot molten material, the generated plasma and the over-critical liquid in the ablation zone. During this interaction, chemical reactions at the surface can take place and cause a persistent change of surface chemistry. Since the surface chemistry is a key aspect for micromachining, the interaction has to be studied to determine whether laser processing in liquids can be a feasible alternative to laser processing under ambient atmospheric conditions while reducing the problem of redeposition. Here, we present the results on the change of surface chemistry by laser ablation in liquid of a pristine silicon substrate. The micromachining process is either performed in an aqueous or gaseous environment and studied in dependence of laser intensity. The changes in surface chemistry are evaluated by micro-Raman spectroscopy and EDX.
Shape-Constrained Method of Remote Sensing Monitoring of Marine Raft Aquaculture Areas on Multitemporal Synthetic Sentinel-1 Imagery

Large-scale and periodic remote sensing monitoring of marine raft aquaculture areas is significant for scientific planning of their layout and for promoting sustainable development of marine ecology. Synthetic aperture radar (SAR) is an important tool for stable monitoring of marine raft aquaculture areas since it is all-weather, all-day, and cloud-penetrating. However, the scattering signal of marine raft aquaculture areas is affected by speckle noise and sea state, so their features in SAR images are complex. Thus, it is challenging to extract marine raft aquaculture areas from SAR images. In this paper, we propose a method to extract marine raft aquaculture areas from Sentinel-1 images based on the analysis of the features for marine raft aquaculture areas. First, the data are preprocessed using multitemporal phase synthesis to weaken the noise interference, enhance the signal of marine raft aquaculture areas, and improve the significance of the characteristics of raft aquaculture areas. Second, the geometric features of the marine raft aquaculture area are combined to design the model structure and introduce the shape constraint module, which adds a priori knowledge to guide the model convergence direction during the training process. Experiments verify that the method outperforms the popular semantic segmentation model with an F1 of 84.52%.
Investigation on methanol induced cell wall degradation in plants using home-built LSFM system

The cell wall is the hard layer outside the cell membrane, composed of cellulose, and provides strength to the plant body. Moreover, the tensile strength of the cell wall helps in maintaining the mechanical rigidity and expansion of cells. Any deformation to the cell wall will affect the vitality of the plants. Studies reveal the solubility of cellulose in alcohols, and here we report the study of endurance of cell wall when exposed to methanol. We employed a home-built light sheet microscopy system to visualize the time-dependent deterioration of the cell wall due to the intake of methanol by the plant. The studies were repeated for different concentrations of methanol and show a concentration-dependent degradation curve.
Log Evaluation About Formation Pore Pressure of Boxing Subsag

In view of the distinguishing feature of low-porosity and permeability layer with strong aeolotropism,complex pore configuration,variegated lithology and complex relationship of oil and water,we use Eaton method to compute the formation pore pressure on the basis of recognizing the contributing factor of abnormal formation pressure.The analysis demonstrates:(1) The evaluation method about formation pore pressure based on Eaton model is perfectly adaptable to sand shale layer with low-porosity and permeability in Boxing Subsag,the predicted result with which is of high accuracy and can meet the need of logging operations;(2) Coefficient of compaction correction is an important parameter to prediction accuracy about formation pore pressure,when the limit of formation depth is big,the coefficient of compaction correction C is not a constant but exponentially increases with the depth.
AFM Characterization of Temperature Effect on the SU-8 Mechanical and Tribological Properties

This study presents the effect of temperature on the mechanical and tribological properties of SU-8 polymer. The temperature of investigated samples increasing during testing and the variation of mechanical and tribological properties were monitored. The samples for tests were SU-8 hard baked at different temperatures. The hard bake temperature changes the mechanical and tribological properties of polymers. The aim of this research work is the reliability design improvement of SU-8 microstructures from electro-thermally actuated devices where a thermal gradient produces the softening and modification of SU-8 behavior. As a function of the hard baked temperature, different mechanical and tribological properties were experimentally determined using the atomic force microscopy (AFM) technique. The mechanical properties of interest are the modulus of elasticity and hardness. The investigated tribological properties involve the adhesion and friction forces. The modulus of elasticity and hardness decrease if the operating temperature increases based on the thermal relaxation of material and their viscoelastic behavior. The adhesion force between AFM tip and investigated samples increases if the operating temperature increases, respectively. The same evolution was experimentally observed in the case of friction force. Moreover, for the same testing temperature, the modulus of elasticity and hardness increase, and the adhesion and friction forces decrease if the SU-8 is hard baked at high temperature.
Evaluation of Acoustic Noise Level and Impulsiveness Inside Vehicles in Different Traffic Conditions

Recently, the issue of sound quality inside vehicles has attracted interest from both researchers and industry alike due to health concerns and also to increase the appeal of vehicles to consumers. This work extends the analysis of interior acoustic noise inside a vehicle under several conditions by comparing measured power levels and two different models for acoustic noise, namely the Gaussian and the alpha-stable distributions. Noise samples were collected in a scenario with real traffic patterns using a measurement setup composed of a Raspberry Pi Board and a microphone strategically positioned. The analysis of the acquired data shows that the observed noise levels are higher when traffic conditions are good. Additionally, the interior noise presented considerable impulsiveness, which tends to be more severe when traffic is slower. Finally, our results suggest that noise sources related to the vehicle itself and its movement are the most relevant ones in the composition of the interior acoustic noise.
Application of Absorbable Suture in Strabismus Correction and Nursing Management Advantage of Watson Care Theory in Perioperative Period

Objectives The article is aimed at investigating the suture effect of absorbable suture in strabismus correction and the advantage of Watson care theory. Methods 148 children with strabismus were enrolled in this study and divided into two groups. The control group received routine nursing, and on this basis, the observation group was given Watson care theory nursing and eye muscle suture with absorbable sutures. The degree of cooperation (induction compliance checklist (ICC) score), negative emotion (modified Yale preoperative anxiety scale (m-YAPS) score), pain (face pain scale (FPS) score), quality of nursing management, parents' satisfaction, clinical efficacy, and adverse reactions were compared. Results After nursing and surgical treatment, all children's strabismus symptoms were improved, and the improvement of strabismus and visual function in the observation group was more obvious. Watson care nursing can effectively improve the nursing management quality, parents' satisfaction, children's negative emotions, and treatment compliance. Conclusions As a result, Watson care theory nursing is of great significance for improving the relationship between nurses and patients and building a harmonious hospital.
USER ACCEPTABILITY AS THE BASIS FOR PERFORMANCE- BASED SPECIFICATIONS FOR A MAJOR CYCLING FACILITY

This paper describes how user expectations are a key to driver acceptability of asset maintenance standards. Specifications for a build-operate-transfer contract for a premier bicycle and multi-use trail have been developed and include provisions for roughness, texture and skid resistance. Roughness standards have been established on the basis of user acceptability ratings. However, riders are generally not aware of the latter two variables, so these aspects of the specifications have been determined on the basis of the long-term performance of existing assets. The paper decides the process for selecting sites, measuring the physical characteristics of the paths at these sites, obtaining cyclists ratings, and correlating the cyclist ratings with the physical measurements. In the case of asphalt paths, user ratings were correlated with roughness measured by the ARRB Walking Profiler. This was not appropriate for concrete paths, due to the very limited range of roughness and to discomfort being caused by vertical displacement between slabs rather than roughness per se. To establish acceptability limits for concrete paths, riders rated the discomfort of riding over chipboard sheets of different thicknesses. Different specifications are proposed for asphalt and concrete paths, but aimed at providing equivalent comfort levels, to the extent that this is possible. Application of this procedure to other types of facility is discussed.
Simulation of Boundary Layers and Heat Transfer in the Entrance Region of Pipes

Lappeenranta University of Technology Department of Information Technology Srujal P. Shah Simulation of Boundary Layers and Heat Transfer in the Entrance Region of Pipes Master’s Thesis 2007 62 pages, 23 figures, 3 tables and 1 appendix Examiners: Professor Heikki Haario Professor Timo Hyppanen