Short-Term Traffic Flow Forecasting Model Based on GA-TCN

Traffic flow forecasting is the key to an intelligent transportation system (ITS). Currently, the short-term traffic flow forecasting methods based on deep learning need to be further improved in terms of accuracy and computational efficiency. Therefore, a short-term traffic flow forecasting model GA-TCN based on genetic algorithm (GA) optimized time convolutional neural network (TCN) is proposed in this paper. The prediction error was considered as the fitness value and the genetic algorithm was used to optimize the filters, kernel size, batch size, and dilations hyperparameters of the temporal convolutional neural network to determine the optimal fitness prediction model. Finally, the model was tested using the public dataset PEMS. The results showed that the average absolute error of the proposed GA-TCN decreased by 34.09%, 22.42%, and 26.33% compared with LSTM, GRU, and TCN in working days, while the average absolute error of the GA-TCN decreased by 24.42%, 2.33%, and 3.92% in weekend days, respectively. The results indicate that the model proposed in this paper has a better adaptability and higher prediction accuracy in short-term traffic flow forecasting compared with the existing models. The proposed model can provide important support for the formulation of a dynamic traffic control scheme.

Prediction of Geometric Characteristics of Melt Track Based on Direct Laser Deposition Using M-SVR Algorithm

Geometric characteristics provide an important means for characterization of the quality of direct laser deposition. Therefore, improving the accuracy of a prediction model is helpful for improving deposition efficiency and quality. The three main input variables are laser power, scanning speed, and powder-feeding rate, while the width and height of the melt track are used as outputs. By applying a multi-output support vector regression (M-SVR) model based on a radial basis function (RBF), a non-linear model for predicting the geometric features of the melt track is developed. An orthogonal experimental design is used to conduct the experiments, the results of which are chosen randomly as training and testing data sets. On the one hand, compared with single-output support vector regression (S-SVR) modeling, this method reduces the root mean square error of height prediction by 22%, with faster training speed and higher prediction accuracy. On the other hand, compared with a backpropagation (BP) neural network, the average absolute error in width is reduced by 5.5%, with smaller average absolute error and better generalization performance. Therefore, the established model can provide a reference to select direct laser deposition parameters precisely and can improve the deposition efficiency and quality.

Analysis of 14C, 13C and Aspartic Acid Racemization in Teeth and Bones to Facilitate Identification of Unknown Human Remains: Outcomes of Practical Casework

The identification of unknown human remains represents an important task in forensic casework. If there are no clues as to the identity of the remains, then the age, sex, and origin are the most important factors to limit the search for a matching person. Here, we present the outcome of application of so-called bomb pulse radiocarbon (14C derived from above-ground nuclear bomb tests during 1955–1963) analysis to birthdate human remains. In nine identified cases, 14C analysis of tooth crowns provided an estimate of the true date of birth with an average absolute error of 1.2 ± 0.8 years. Analysis of 14C in tooth roots also showed a good precision with an average absolute error of 2.3 ± 2.5 years. Levels of 14C in bones can determine whether a subject has lived after 1955 or not, but more precise carbon turnover data for bones would be needed to calculate date of birth and date of death. Aspartic acid racemization analysis was performed on samples from four cases; in one of these, the year of birth could be predicted with good precision, whereas the other three cases are still unidentified. The stable isotope 13C was analyzed in tooth crowns to estimate provenance. Levels of 13C indicative of Scandinavian provenance were found in known Scandinavian subjects. Teeth from four Polish subjects all showed higher 13C levels than the average for Scandinavian subjects.

An Active Balancing Method Based on SOC and Capacitance for Lithium-Ion Batteries in Electric Vehicles

An active balancing method based on the state of charge (SOC) and capacitance is presented in this article to solve the inconsistency problem of lithium-ion batteries in electric vehicles. The terminal voltage of each battery is collected first. Then, each battery SOC is accurately estimated by an extended Kalman filter (EKF) algorithm. In the experiment, the maximum absolute error of SOC evaluation is only 0.0061, and the mean absolute error is 0.0013 when the initial battery SOC is clear. Meanwhile, the maximum absolute error of SOC evaluation is 0.5 and the average absolute error of SOC is 0.0015 when the initial battery SOC is not clear. Afterward, an active balancing circuit based on the estimated battery SOC and capacitance is designed. The energy of capacitance is charged by the battery whose SOC is higher than the other batteries through the circuit to avoid the battery being overcharged. Then, the SOC of batteries gradually turn consistent. In the simulation experiment, the SOC difference of batteries is 7% before the balancing. Meanwhile, the SOC difference of batteries reduces to 0.02% after the balancing and the consuming time is merely 272s, which manifests that the proposed balancing method has a fast balancing speed and better balancing efficiency.

Study of a Privacy Preserving Logistic Regression Algorithm (PPLRA) For Data Privacy in the Context of Big Data

Regression algorithms are commonly used in machine learning. Based on encryption and privacy protection methods, the current key hot technology regression algorithm and the same encryption technology are studied. This paper proposes a PPLAR based algorithm. The correlation between data items is obtained by logistic regression formula. The algorithm is distributed and parallelized on Hadoop platform to improve the computing speed of the cluster while ensuring the average absolute error of the algorithm.

Simple and accurate calculation model of viscosity for supercritical CO2

The high-precision calculation method suitable for the viscosity of supercritical CO2 is reported. The modified LBC model, modified DS model and Ehsan empirical formula are selected to calculate viscosity. The error analysis result shows that the average absolute error of the modified DS model is the lowest, which can be applied to the engineering calculation. The LBC model is suitable at 7.5–25MPa and the Ehsan empirical formula has high accuracy at 25–60MPa. Then the viscosity plate is drawn at temperature range of 310–800K and pressure range of 7.5–60MPa, which can be applied in carbon dioxide resources recovery and utilization of technology.

Mobile Remote Photoplethysmography: A Real-Time Realization Perspective

Remote photoplethysmography (rPPG) sensors have attracted a significant amount of attention as they enable the remote monitoring of instantaneous heart rates (HRs) and thus do not require any additional devices to be worn on fingers or wrists. In this study, we mounted rPPG sensors on a robot for active and autonomous instantaneous HR (R-AAIH) estimation. Subsequently, we proposed the algorithm providing accurate instantaneous HRs, which can be performed in real time with vision and robot manipulation algorithms. By simplifying the extraction of facial skin images using saturation (S) values in the HSV color space, and selecting pixels based on the most frequent S value on the face image, we achieved reliable HR assessment. The results of the proposed algorithm using the R-AAIH were evaluated by rigorous comparison with the results of existing algorithms on the UBFC-RPPG dataset (n = 42). Our algorithm exhibited an average absolute error (AAE) of 0.71 beats per minute (bpm). The developed algorithm is simple and the processing time is less than 1 s (275 ms for an 8-s window). The algorithm was further validated on our own dataset (BAMI-RPPG dataset [n = 14]) with an AAE of 0.82 bpm.

Estimating biological age by hematological blood parameters

For the estimation of the biological age (BA) of people based on hematological parameters of the clinical blood test there were used MLR and Deep Neural Networks. In the archive of the Institute of Gerontology NAMS of Ukraine there were selected people aged from 20 up to 90 years (440 men and 504 women), who had all hematological parameters within normal limits. When using the MLR method, the multiple correlation coefficients (R) have low values for both men (0.37) and women (0.38). The use of Deep Neural Networks has given good results. The values of the correlation coefficients between BA and chronological age were 0.92 for men and 0.79 for women. The average absolute error in determining BA was 3.68 years for the men and 6.55 years for the women. The developed method for assessing hematological age can be used in clinical practice to identify people with the risk of developing hematological pathology, as well as in population researches. Keywords: biological age, hematological blood parameters, deep neural network

MODEL OF HUMAN METABOLIC AGE

With aging, regular changes develop in metabolism, first of all, these are changes in lipid and carbohydrate metabolism. With accelerated aging, metabolic disorders are more expressed, which leads to the development of metabolic syndrome. The purpose of the work was to develop a method for calculating metabolic age using available clinical tests and to assess the rate of metabolic aging in people with metabolic syndrome. Materials and methods. The study involved 283 apparently healthy people aged 20 to 80 years and 82 people with metabolic syndrome. Anthropometric parameters and biochemical tests were measured for all people included in the study. The formula for calculating metabolic age was obtained by the method of stepwise multiple regression.Results. The calculation of the metabolic age in healthy people according to the formula we obtained showed that the average absolute error is 6.01 years. In 20.5% of people with metabolic syndrome, metabolic age exceeds chronological age by more than 10 years. At the same time, in the group of healthy people, the share of such people was only 4.2%. Conclusions. The method we have developed for assessing the rate of metabolic aging has a sufficiently high accuracy and can be used to assess the risk of developing metabolic syndrome and other agerelated pathology.

Design and Calibration of Moisture Sensor Based on Electromagnetic Field Measurement for Irrigation Monitoring

Soil moisture control is crucial to assess irrigation efficiency in green areas and agriculture. In this paper, we propose the design and calibration of a sensor based on inductive coils and electromagnetic fields. The proposed prototypes should meet a series of requirements such as low power consumption, low relative error, and a high voltage difference between the minimum and maximum moisture. We tested different prototypes based on two copper coils divided into two different sets (P1–P15 and NP1–NP4). The prototypes have different characteristics: variations in the number and distribution of spires, existence or absence of casing, and copper wires with a diameter of 0.4 or 0.6 mm. In the first set of experiments carried out in commercial soil, the results showed that the best prototypes were P5, P8, and P9. These prototypes were used in different types of soils, and P8 was selected for the subsequent tests. We carried the second set of experiments using soil from an agricultural field. Based on the data gathered, mathematical models for the calibration of prototypes were obtained and verified. In some cases, two equations were used for different moisture intervals in a single prototype. According to the verification results, NP2 is the best prototype for monitoring the moisture in agricultural lands. It presented a difference in induced voltage of 1.8 V, at 500 kHz, between wet and dry soil with a maximum voltage of 5.12 V. The verification of the calibration determined that the calibration using two mathematical models offers better results, with an average absolute error of 2.1% of moisture.