High Identification Efficiency Research Articles

Shared alleles and genetic structures in different Thai domestic cat breeds: the possible influence of common racial origins

Over hundreds of years, cats have been domesticated and selectively bred, resulting in numerous pedigreed breeds expedited by recent cat shows and breeding associations. Concerns have been raised about the limited breeding options and the genetic implications of inbreeding, indicating challenges in maintaining genetic diversity and accurate identification in purebred cats. In this study, genetic variability and structure were examined in 5 Thai domestic cat breeds using 15 microsatellite markers and mitochondrial DNA (mtDNA) D-loop sequencing. In total, 184 samples representing the Wichien Maat (WCM), Suphalak (SL), Khao-Manee (KM), Korat (KR), and Konja (KJ) breeds were analyzed. High genetic diversity (Ho and He > 0.5) was observed in all breeds, and mtDNA analysis revealed two primary haplogroups (A and B) that were shared among all domestic cat breeds in Thailand and globally. However, minor differences were observed between Thai domestic cat breeds based on clustering analyses, in which a distinct genetic structure was observed in the WCM breed. This suggests that allele fixation for distinctive morphological traits has occurred in Thai domestic cat breeds that emerged in isolated regions with shared racial origins. Analysis of relationships among individuals within the breed revealed high identification efficiency in Thai domestic cat breeds (P(ID)sibs < 10−4). Additionally, diverse and effective individual identification can be ensured by optimizing marker efficiency by using only nine loci. This comprehensive genetic characterization provides valuable insights into conservation strategies and breeding practices for Thai domestic cat breeds.

Bridge damage identification based on synchronous statistical moment theory of vehicle–bridge interaction

AbstractConsidering the weak noise resistance and low identification efficiency of traditional bridge damage identification methods, a data‐driven approach based on synchronous statistical moment theory and vehicle–bridge interaction vibration theory is proposed. This method involves two main steps. First, a two‐axle test vehicle is used to collect acceleration response signals synchronously from adjacent designated measurement points while stationary. This operation is repeated to calculate the second‐order statistical moment curvature (SOSMC) difference of entire bridge points corresponding signals in different states. By comparing with the reference value, the preliminary damage location of the bridge can be obtained. Second, the first‐order modal shape curve is constructed using the second‐order statistical moment (SOSM). The refined identification of bridge damage is then based on an improved direct stiffness back calculation of the bridge's stiffness. This article proposes the synchronization theory for the first time and combines it with the statistical moment clustering method, forming an innovative approach to obtaining structural vibration modes. The effectiveness of this method has been well validated through numerical simulations with different parameters and on‐site bridge tests. The research results indicate that SOSMC indicators have better noise resistance and higher recognition efficiency in identifying damage locations, compared to modal curvature and flexibility curvature indicators. Additionally, compared to transfer rate and random subspace methods, the SOSM method results in smaller error and higher identification efficiency.

Integration of Deep Learning with Vibration-Based Identification Method

The vibration-based identification method has long been used as an alternative to mechanical tests for material characterization. Its non-destructive nature, high efficiency in parameter identification of anisotropic materials and its ability to achieve structure-level homogenization make it very attractive for both academic community and industry. Due to the absence of explicit mapping from the result of vibration test to elastic constants, conventional vibration-based identification approaches generally rely on the minimization of a cost function built on modal properties or Frequency Response Function (FRF) curve. In this paper, a direct relation from FRFs to mechanical properties is established by deep neural network, which offers a new perspective to solve the inverse identification problem. Specifically, this mapping is approximately built with Multilayer Perceptrons (MLPs) and the network’s weights are learned from massive training data. The training data-i.e., the FRF curves, is generated by a finite element model whose input parameters are selected by Latin Hypercube Sampling (LHS). After training, the obtained model is validated by dataset unseen during training process and will be further used to predict mechanical properties of materials from FRFs obtained by vibration test. The effectiveness and efficiency of the developed method are demonstrated on the case study of an aluminum plate. Both the Young’s modulus and Poisson’s ratio are retrieved with high accuracy.

Inverse Estimation of Dynamic Heat Flux Using Proportional Integral Derivative Control

To improve long-term dynamic heat flux prediction using an embedded thermocouple, a novel inverse estimation approach using a proportional integral derivative (PID) control scheme is proposed. A feedforward model of the discrete integer-order transfer function was identified from a one-dimensional dynamic heat-flux sensor using the calibrated heat flux with varying amplitudes of the pseudo-random binary sequence and its single-temperature response data. Using the model and unit-step temperature response as the controlled plant and reference input signal, the PID controller with a low-pass filter was designed. Using the on-site temperature measurement data as the reference signal instead of the unit-step temperature, the time-history control output values, that is, the inverse estimated dynamic heat flux of the designed PID controller, were obtained. The comparison of the test results and the discussion indicate that 1) the proposed inverse method avoids the impact of the thermal inertia of the thermocouple and significantly alleviates the effects of measurement noise, and 2) in addition to high identification efficiency and measurement fidelity, further improvement in the effective measuring time of the dynamic heat flux was observed.

An Improved Crack Identification Method for Asphalt Concrete Pavement

The results of high-precision asphalt concrete pavement crack identification can provide help for pavement maintenance. Therefore, methods of image feature enhancement and crack identification of asphalt concrete pavement cracks are proposed. First of all, we used an industrial CCD camera mounted on a vehicle to collect an asphalt concrete pavement crack image. Then, after using the NeighShrink algorithm to denoise the acquired image, a fractional differential image enhancement algorithm was designed based on image feature blocks to enhance the image features. On this basis, crack characteristics were segmented and processed by watershed algorithm. Through crack direction identification and crack parameter extraction, crack distribution direction, crack length and width and other parameters of asphalt concrete pavement were obtained in order to achieve accurate identification of asphalt concrete pavement cracks. The experiment found that this method can effectively remove noise information from asphalt concrete crack images; after applying this method, the image entropy value of each image was improved, with a minimum improvement of 0.38 and a maximum improvement of 1.98. The time consumed by this method in identifying cracks in asphalt concrete pavement varied between 1.4 s and 2.4 s. When identifying the length of cracks in asphalt concrete pavement, the maximum deviation value was only 0.47 mm; when identifying the width of cracks in asphalt concrete pavement, the maximum deviation value was only 0.31 mm. The above results indicate that by enhancing the image features of asphalt concrete cracks, this method achieves more accurate identification results for crack distribution direction, length and width values, with high identification efficiency and good application effect.

SOYBEAN PEST IDENTIFICATION BASED ON CONVOLUTIONAL NEURAL NETWORK AND TRANSFER LEARNING

Despite the fact that the ensemble classifier improved classification precision by integrating texture and colour. However, the significant image preparation process is a laborious and time-consuming. Manually depicting constrained feature extraction can result in a semantic void in a picture. These limits cause inaccuracy of agricultural disease identification. Thus, this study proposes a soybean pest detection method based on a hybrid of Transfer learning and pyramidal convolutional neural networks that can identify soybean pests quickly and accurately on small sample sets. Bean borer, soybean poison moth, mite, stink bug, and pod borer photographs are first pre-processed using standard data improvement methods, and then manually categorized into six groups based on pest characteristics. The weight parameters from the VGG16 model trained on the ImageNet image dataset were then transferred to the recognition of soybean pests using the transfer learning method, and the VGG16 convolutional and pooling layers were used as feature extraction layers, while the top layer was redesigned as a pyramidal convolutional layer, an average pooling layer, and a SoftMax output layer, with some of the convolutional layers frozen during training. According to the testing statistics, the model's average test accuracy is 98.23%, and the model size is only 95.4 M. For bean borer, soybean poison moth, mite, skewed night moth, stink bug, and bean pod moth, the model's recognition accuracy is 96.4, 97.78, 98.12, 98.4, 99.56, and 99.16, respectively. The results of the experiments show that the method has a high identification efficiency and a good recognition effect.

VERI: A Large-scale Open-Source Components Vulnerability Detection in IoT Firmware

IoT device manufacturers integrate open-source components (OSCs) to serve necessary and common functions for facilitating firmware development. However, outdated versions of OSC conceal N-day vulnerabilities and continue to function on IoT devices. The security risks can be predicted once we can identify the OSC versions employed in the firmware. Existing works make attempts at OSC version identification but fail to perform vulnerability detection on a large-scale IoT firmware due to i) unsuitable version identification method for IoT firmware scenario. ii) the lack of a large-scale version-vulnerability relation database. To this end, we propose a system VERI for large-scale vulnerability detection based on lightweight version identification. First, for OSC version identification, VERI leverages symbolic execution with static analysis to identify exact OSC versions even though there are many version-like strings in OSC. Second, VERI employs a deep learning-based method to extract OSC names and vulnerable version ranges from vulnerability descriptions, constructs and maintains an OSC version-vulnerability relation database to serve the vulnerability detection. Finally, VERI polls the relation database to confirm the N-day security risk of the OSC with identified version. The evaluation results show that VERI achieves 96.43% accuracy with high efficiency in OSC version identification. Meanwhile, the deep learning model accurately extracts the OSC names and versions from vulnerability descriptions dataset with 97.19% precision and 96.56% recall. Based on the model, we build a large-scale version-vulnerability relation database. Furthermore, we utilize VERI to conduct a large-scale analysis on 28,890 firmware and find 38,654 vulnerable OSCs with 266,109 N-day vulnerabilities, most of which are with high risks. From the detection results, we find that after the official patch for the vulnerability is released, manufacturers delay an average of 473 days to patch the firmware.

An Improved Approach for Reader Anti-Collision in Industrial Internet of Things

Radio-frequency identification (RFID) technology has been used in numerous applications, e.g., supply chain management and inventory control. This paper focuses on the practically important problem of reader-to-reader collision in large-scale RFID systems. There are many technical challenges during the existing work such as high computational complexity and low identification efficiency. To tackle the above challenges, this paper proposes a novel reader anti-collision protocol optimized by minimal reverse weight (NRA-MRW), which includes two phases. Extensive simulation results show that our proposed NRA-MRW can improve the identification efficiency and system throughput over the existing protocols by 25.2% and 12.3%, respectively.

Identification Method for Cone Yarn Based on the Improved Faster R-CNN Model

To solve the problems of high labor intensity, low efficiency, and frequent errors in the manual identification of cone yarn types, in this study five kinds of cone yarn were taken as the research objects, and an identification method for cone yarn based on the improved Faster R-CNN model was proposed. In total, 2750 images were collected of cone yarn samples in real of textile industry environments, then data enhancement was performed after marking the targets. The ResNet50 model with strong representation ability was used as the feature network to replace the VGG16 backbone network in the original Faster R-CNN model to extract the features of the cone yarn dataset. Training was performed with a stochastic gradient descent approach to obtain an optimally weighted file to predict the categories of cone yarn. Using the same training samples and environmental settings, we compared the method proposed in this paper with two mainstream target detection algorithms, YOLOv3 + DarkNet-53 and Faster R-CNN + VGG16. The results showed that the Faster R-CNN + ResNet50 algorithm had the highest mean average precision rate for the five types of cone yarn at 99.95%, as compared with the YOLOv3 + DarkNet-53 algorithm with a mean average precision rate that was 2.24% higher and the Faster R-CNN + VGG16 algorithm with a mean average precision that was 1.19% higher. Regarding cone yarn defects, shielding, and wear, the Faster R-CNN + ResNet50 algorithm can correctly identify these issues without misdetection occurring, with an average precision rate greater than 99.91%.

DNA barcoding of Chironomidae from the Lake Skadar region: Reference library and a comparative analysis of the European fauna

AbstractAimsThe main aim of this study was to fill a gap in barcoding data of the European Chironomidae of the Balkan region, developing and testing the efficiency of a reference DNA barcode library for ancient Lake Skadar basin species (Montenegro/Albania), a region of Europe never before subjected to barcoding studies on Chironomidae. Another aim was to test the efficiency of DNA barcoding for the identification of European Chironomidae, including the estimation of optimal identification thresholds, using &gt;12,000 barcodes.LocationLake Skadar basin and adjacent area (Montenegro/Albania).MethodsThrough this study, 770 individuals of Chironomidae from the Lake Skadar region were barcoded, both at adult and pre‐imaginal stages. Adults were morphologically identified, while larvae were assigned to species by molecular identification, using different methods, of which the efficiency was tested, for a total of 97 different barcoded species.ResultsThe identification efficiency of the reference dataset developed for the Lake Skadar region was 98.6%, a value in line with that obtained when the identification efficiency for European Chironomidae was evaluated (95.8%), which confirms the accuracy of DNA barcoding for the identification of these insects. Moreover, we found that the optimal threshold for the molecular identification of the family is 1.6% nucleotide distance, though more specific thresholds are suggested for the identification of species belonging to Chironomidae subfamilies, since they are related to lower identification errors than to the use of a general threshold. The analysis of inconsistency between molecular and morphological identification shed light on taxonomic issues within European Chironomidae. Previously postulated species synonyms were confirmed, and also further cases requiring deeper investigation were detected.Main conclusionsOur de novo DNA barcode library was shown to have a high identification efficiency. Taxon‐specific thresholds increase the efficacy of molecular identification. Hypothesized species synonyms could be validated through molecular techniques.

IDENTIFICATION BY SSR AND SRAP MARKERS AND HETEROSIS ANALYSIS OF F1 HYBRIDS (Medicago ruthenica L.)

Medicago ruthenica L. could be used as the crossbreeding material in forage crops to provide rich resistance gene resources based on its strong resistance to adversity stress. This study aimed to identify the authenticity and analyze heterosis of the intraspecific hybrids acquired from two Medicago ruthenica L. materials. The results showed 85 true hybrids in 118 F1 progenies identified by one SSR marker and five SRAP markers. Besides, the SRAP markers (13.89%) indicated higher identification efficiency than SSR markers (7.69%), and the rate of true hybrids in HZ population (100%) was higher than that in ZH population (36.54%). The six agronomic traits varied to different degrees, and their variation coefficients ranged from 18.53% to 45.72% in 13 hybrids of ZH population in 2019 and 2020. Moreover, ZH7 and ZH8 presented excellent agronomic performance, which could be used as candidate materials for further research. The mid-parent heterosis (Hm) of all agronomic traits was between -20.55% and 36.46%, and heterobeltiosis (Hh) showed negative values of 13 hybrids.

Non-intrusive Load Identification Method based on SAGOA-GMM Algorithm

Non-intrusive Load Identification play an important role in daily life. It can monitor and predict grid load while statistics and analysis of user electricity information. Aiming at the problems of low non-intrusive load decomposition ability and low precision when two electrical appliances are started and stopped at the same time, a new type of clustering and decomposition algorithm is proposed. The algorithm first analyses the measured power and use DBSCAN to filter out the noise of the collected data. Secondly, the remaining power points are clustered using the Adaptive Gaussian Mixture Model (AGMM) to obtain the cluster centres of the electrical appliances, and finally correlate the corresponding current waveform to establish a load characteristic database. In terms of load decomposition, a mathematical model was established for the magnitude of the changing power and current. The Grasshopper optimization algorithm (GOA) is optimized by introducing simulated annealing (SA) to identify and decompose electrical appliances that start and stop at the same time. The result of the decomposition is checked by the current similarity test to determine whether the result of the decomposition is correct, thereby improving the recognition accuracy. Experimental data shows that the combination of DBSCAN and GMM can can identify similar power characteristics. The introduction of SA makes up for the weakness of GOA and gives full play to the advantages of GOA's high identification efficiency. Finally, the test is carried out through the load detection data of the simultaneous start and stop of the two equipment. The test results show that the proposed method can effectively identify the simultaneous start and stop of two loads and can solve the problem of low recognition rate caused by the similar load power, which lays the foundation for the development of non-intrusive load identification in the future.

Axle Configuration and Weight Sensing for Moving Vehicles on Bridges Based on the Clustering and Gradient Method

Traffic information, including vehicle weight and axle spacing, is vital for bridge safety. The bridge weigh-in-motion (BWIM) system remotely estimates the axle weights of moving vehicles using the response measured from instrumented bridges. It has been proved more accurate and durable than the traditional pavement-based method. However, the main drawback of conventional BWIM algorithms is that they can only identify the axle weight and the information of axle configuration (the number of axles and axle spacing) is required to be determined using an extra device in advance of the weight identification procedure. Namely, dedicated sensors (pressure-sensitive sensors placed on the deck surface or under the soffit of a bridge) in addition to weighing sensors must be adopted for identifying the axle configuration, which significantly decreases the utility, feasibility, and economic efficiency of BWIM technology. In this study, a new iterative procedure simultaneously identifying axle spacing as well as axle weights and gross weights of vehicles is proposed. The novel method is based on k-means clustering and the gradient descent method. In this method, both the axle weight and the axle location are obtained by using the same global response of bridges; thus the axle detectors are no longer required, which makes it economical and easier to be implemented. Furthermore, the proposed optimization method has good computational efficiency and thus is practical for real-time application. Comprehensive numerical simulations and laboratory experiments based on scaled vehicle and bridge models were conducted to verify the proposed method. The identification results show that the proposed method has good accuracy and high computational efficiency in axle spacing and axle weight identification.

Indoor Cardiovascular Health Monitoring System under COVID 19 Situations

In this article, we propose a health monitoring system, especially focused on detecting myocardial infarction chances and raising the alarm when required. The device would be able to identify malfunctioning of the heart using the ECG signals taken using ARDUINO-based portable and affordable sensors. Here, we have described the development of two major schemes of health monitoring. We have developed a method of identifying the maloperation of the heart by using the second lead of the ECG signal. This method is based on a modified Poincaré analysis, which effectively distinguishes the diseased heart ECG from the normal heart ECG signals. The Poincaré-based scheme is tested using five different diseased ECG signals from the PTB database, and a high disease identification efficiency of 82% is achieved. Here we further propose implementing this analysis within the ARDUINO-based health monitoring board so that patients would get an opportunity to monitor their heart condition at some regular intervals staying at home and follow up with a physician if any abnormality is detected. This entire scheme is more useful, especially under this COVID 19, pandemic situation when moving outside the home is a challenge in itself. Hence, the proposed device would be helpful during this global crisis, especially for people residing in rural and semi-urban regions.

Muon reconstruction and identification efficiency in ATLAS using the full Run 2 pp collision data set at sqrt{s}=13 TeV

This article documents the muon reconstruction and identification efficiency obtained by the ATLAS experiment for 139 hbox {fb}^{-1} of pp collision data at sqrt{s}=13 TeV collected between 2015 and 2018 during Run 2 of the LHC. The increased instantaneous luminosity delivered by the LHC over this period required a reoptimisation of the criteria for the identification of prompt muons. Improved and newly developed algorithms were deployed to preserve high muon identification efficiency with a low misidentification rate and good momentum resolution. The availability of large samples of Zrightarrow mu mu and J/psi rightarrow mu mu decays, and the minimisation of systematic uncertainties, allows the efficiencies of criteria for muon identification, primary vertex association, and isolation to be measured with an accuracy at the per-mille level in the bulk of the phase space, and up to the percent level in complex kinematic configurations. Excellent performance is achieved over a range of transverse momenta from 3 GeV to several hundred GeV, and across the full muon detector acceptance of |eta |<2.7.

Railway dangerous goods transportation system risk identification: Comparisons among SVM, PSO-SVM, GA-SVM and GS-SVM

In this paper, three algorithms are applied to obtain the parameters of Radial Basis Function (RBF) kernels of Support Vector Machines (SVM), which include: PSO (Particle Swarm Optimization), GA (Genetic Algorithm) and GS (Grid Search). The three improved SVM approaches are applied to identify the risk of railway dangerous goods transportation system (RDGTS). The statistical occurrence frequency of each sub-risk indicator of the happened RDGTS accidents is used as the basis of experts’ scores, the experts’ scores are presented in interval numbers form, which are used as the inputs of the four approaches. The accuracy rate, optimization time consuming, Mean Square Error (MSE), Receiver Operating Characteristic Curve (ROC) and Area Under Curve (AUC) are used as the evaluation indexes of the identification results. The comparison studies are conducted by using SVM with linear kernel (SVM-L) and SVM with polynomial kernel (SVM-P), respectively. By using such new methodology, the risk identification problem (evaluation problem) is transferred into a classification problem with faster identification speed, higher identification efficiency and higher accuracy. The identification results show that: GS-SVM is the optimal approach to identify the risk factors of Human; SVM is the optimal approach to identify the risk factors of Machine, Materials, Environment and Management. SVM has the shortest optimization time consuming, GA-SVM has the highest accuracy, hence, SVM and GA-SVM are better to applied to identify the risk of RDGTS. The optimization time consuming of all models is no more than 5 s, which means the RDGTS risk identification results could be obtained fast and high-efficiently and the mental strength for researchers can be reduced by using the SVM and improved SVM models. For the risk identification results considering MSE and AUC, GS-SVM is the most accurate and best classification algorithm, and the results based on SVM-P is better than the results based on SVM-L. The results based on PSO-SVM, GA-SVM and GS-SVM have better accuracy and reliability than that based on SVM-L or SVM-P, which means the PSO-SVM, GA-SVM and GS-SVM based risk identification approaches are practicable.

Molecular characterization of marine and coastal fishes of Bangladesh through DNA barcodes.

This study describes the molecular characterization of marine and coastal fishes of Bangladesh based on the mitochondrial cytochrome c oxidase subunit I (COI) gene as a marker. A total of 376 mitochondrial COI barcode sequences were obtained from 185 species belonging to 146 genera, 74 families, 21 orders, and two classes of fishes. The mean length of the sequences was 652 base pairs. In Elasmobranchii (Sharks and rays), the average Kimura two parameter (K2P) distances within species, genera, families, and orders were 1.20%, 6.07%, 11.08%, and 14.68%, respectively, and for Actinopterygii, the average K2P distances within species, genera, families, and orders were 0.40%, 6.36%, 14.10%, and 24.07%, respectively. The mean interspecies distance was 16‐fold higher than the mean intraspecies distance. The K2P neighbor‐joining (NJ) trees based on the sequences generally clustered species in accordance with their taxonomic position. A total of 21 species were newly recorded in Bangladesh. High efficiency and fidelity in species identification and discrimination were demonstrated in the present study by DNA barcoding, and we conclude that COI sequencing can be used as an authentic identification marker for Bangladesh marine fish species.

A Method for Identifying Transverse and Thickness Compressive Modulus of Glass Fiber Reinforced Composites Based on DIC

In this paper, the short beam shear (SBS) test combined with digital image correlation (DIC) and finite element model updating (FEMU) method is conducted for identification the constitutive parameters of unidirectional composite laminates by minimizing the objective function which is established based on the variance of measured strain and numerical calculated strain. This method has the advantages of insensitive to initial value and high identification efficiency. By changing the test parameters, specimens with different principal planes were used in the SBS test and different failure modes achieved. The standard deviation of E22C and E33C was 8.47% and 3.58% respectively. The comparison of the identification results of the same batch of specimens, different principal planes, different failure modes and different ROI shows that the results of constitutive identification using the area directly under the indenter is reliable.

A preliminary simulation study of influence of backsplash on the plastic scintillator detector design in HERD experiment

The plastic scintillator detector (PSD) is one of the detectors in the high energy cosmic radiation detection (HERD) facility, which is designed for gamma-ray detection and a redundant charge measurement. Backsplash will lead to a decrease in PSD’s performance of gamma-ray detection and charge measurement, which should be carefully considered. Two preliminary segmentation schemes of the PSD and two veto strategies have been proposed to suppress the backsplash effect. In this paper, we focus on the influence of the backsplash caused by gamma rays. The gamma-ray trigger efficiency and identification efficiency were studied in the case of different cell sizes and veto strategies, which can provide guidance on the PSD design. A Monte Carlo simulation based on Geant4 has been performed. To simplify the simulation, the PSD is segmented into 1 cm3 cubes which can be easily aggregated into cells with different sizes during analysis. Side_Veto can be used as a baseline design of veto strategy, whereas Smart_Veto can be selected as an upgraded design. Both the PSD bar cell with a width of less than 11 cm and the PSD tile cell with a width of less than 20 cm can achieve a sufficiently high gamma-ray trigger efficiency (> 80%), which realizes the primary goal of the PSD. Meanwhile, both the PSD bar cell with a width of less than 3 cm and the PSD tile cell with a width of less than 20 cm can ensure a sufficiently high gamma-ray identification efficiency (> 80%) for photons up to 800 GeV.

Identification of medicinal plants within the Apocynaceae family using ITS2 and psbA-trnH barcodes.

To ensure the safety of medications, it is vital to accurately authenticate species of the Apocynaceae family, which is rich in poisonous medicinal plants. We identified Apocynaceae species by using nuclear internal transcribed spacer 2 (ITS2) and psbA-trnH based on experimental data. The identification ability of ITS2 and psbA-trnH was assessed using specific genetic divergence, BLAST1, and neighbor-joining trees. For DNA barcoding, ITS2 and psbA-trnH regions of 122 plant samples of 31 species from 19 genera in the Apocynaceae family were amplified. The PCR amplification for ITS2 and psbA-trnH sequences was 100%. The sequencing success rates for ITS2 and psbA-trnH sequences were 81% and 61%, respectively. Additional data involved 53 sequences of the ITS2 region and 38 sequences of the psbA-trnH region were downloaded from GenBank. Moreover, the analysis showed that the inter-specific divergence of Apocynaceae species was greater than its intra-specific variations. The results indicated that, using the BLAST1 method, ITS2 showed a high identification efficiency of 97% and 100% of the samples at the species and genus levels, respectively, via BLAST1, and psbA-trnH successfully identified 95% and 100% of the samples at the species and genus levels, respectively. The barcode combination of ITS2/psbA-trnH successfully identified 98% and 100% of samples at the species and genus levels, respectively. Subsequently, the neighbor joining tree method also showed that barcode ITS2 and psbA-trnH could distinguish among the species within the Apocynaceae family. ITS2 is a core barcode and psbA-trnH is a supplementary barcode for identifying species in the Apocynaceae family. These results will help to improve DNA barcoding reference databases for herbal drugs and other herbal raw materials.