Potential of plant identification apps in urban forestry studies in China:comparison of recognition accuracy and user experience of five apps

Information on species composition of an urban forest is essential for its management.However,to obtain this information becomes increasingly difficult due to limited taxonomic expertise.In this study,we tested the possibility of using plant identification applications running on mobile platforms to fill this vacuum.Five plant identification apps were compared for their potential in identifying urban tree species in China.An online survey was conducted to determine the features of apps that contributed to users’satisfaction.The results show that identification accuracy varied significantly among the apps.The best performer achieved an accuracy of 74.6%at the species level,which is comparable to the accuracy by professionals in field surveys.Among the features of apps,accuracy of identification was the most important factor that contributed to users’satisfaction.However,plant identification apps did not perform well when used on rare species or outside of the regions where they have been developed.Results indicate that plant identification apps have great potential in urban forest studies and management,but users need to be cautious when deciding which one to use.

Brand Origin Recognition Accuracy on High-Involved Products： Cellphone Brands in China as a Case

The competition anaong cellphone brands in the world is getting fiercer and fiercer in 3G era. This paper intends to examine the level of consumers＇ brand origin recognition accuracy of high-involved products （referred to hereafter as BORAx）, investigate the factors facilitating BORAHI, and trace the implications of BORAHHI on brand evaluation, especially in cellphone industry. The BORAHI is measured in China through cellphones as the product object and a consumers＇ cognitive model of BORAhn is built. The paper concludes that： Chinese urban consumers have a fairly high BORAHHI; better-educated consumers demonstrate higher BORAHHI scores for foreign brands; rmle consumers have higher BORAHI scores than fe-rrules consumers; consumers lower in ethnocen-trism exhibit higher level of BORAHI for foreign brands, and ethnocentrism has no effect on BO-RAHI for local brands; international experience is not related to BORAHI for local brands; internation-al experience is positively related to education and income respectively, but it is negatively related to age. This research finds that the consumer behavior in China, one of emerging markets, is significantly different from that in developed countries.

Crop Leaf Disease Recognition Network Based on Brain Parallel Interaction Mechanism

In the actual complex environment,the recognition accuracy of crop leaf disease is often not high.Inspired by the brain parallel interaction mechanism,a two-stream parallel interactive convolutional neural network(TSPI-CNN)is proposed to improve the recognition accuracy.TSPI-CNN includes a two-stream parallel network(TSP-Net)and a parallel interactive network(PI-Net).TSP-Net simulates the ventral and dorsal stream.PI-Net simulates the interaction between two pathways in the process of human brain visual information transmission.A large number of experiments shows that the proposed TSPI-CNN performs well on MK-D2,PlantVillage,Apple-3 leaf,and Cassava leaf datasets.Furthermore,the effect of numbers of interactions on the recognition performance of TSPI-CNN is discussed.The experimental results show that as the number of interactions increases,the recognition accuracy of the network also increases.Finally,the network is visualized to show the working mechanism of the network and provide enlightenment for future research.

Recognition of Bangla Handwritten Number Using Combination of PCA and FIS with the Aid of DWT

The structure of any Bangla numerical character is more complex compared to English numerical character. Two pairs of numerical character in Bangla resembles to be closed and they are: “one and nine” and “five and six”. We found that, handwritten Bangla numerical character cannot be recognized using single machine learning algorithm or discrete wavelet transform (DWT). Above phenomenon motivated us to use combination of DWT, Fuzzy Inference System (FIS) and Principal Component Analysis (PCA) to recognize numerical characters of Bangla in handwritten format. The four lowest spectral components of a preprocessed image are taken using DWT, which is considered as the feature vector to recognize the digits in first phase. The feature vector is then applied to FIS and PCA separately. The combined method provides recognition accuracy of 95.8% whereas application of individual method gives less rate of accuracy. Instead of storing the images itself in a folder, if we can store the feature vector of images achieved from DWT in tabular form. The records of table can be applied in FIS, PCA or other object detection algorithm. Although the technique used in the paper can detect objects with moderate rate of accuracy but can save huge storage against a benchmark database of images. If a tradeoff is made between storage requirements and accuracy of recognition, the model of the paper is preferable compared to other present state-of-art. Another finding of the paper is that, the spectral components of images acquired by DWT only matched with FIS and PCA for classification but do not match properly with unsupervised (K-mean clustering) and supervised (support vector machine) learning.

Comments on Quasicrystals

The discoveries of so-called quasicrystals have broken through the theoretic foundation set up by the classical crystallographic group theory since 1891 and proposed new topics for study of solid structures. Electron diffraction patterns (EDP' s) and high-resolution microscopic (HREM) images have proved invaluable tools of studying the structures of crystals. The recognition and determination of EDP's and HREM images of a real-structure play a key role for understanding the structure. This paper will introduce some new developments about crystallographic group theory and new image processing methods on EDP's and HREM images. Contrary to popular beliefs, the research shows that quasicrystals can be understood (perturbed) complex periodic structures.

基于CsPbBr_(3)-MXene纳米结构的高线性度突触光电晶体管用于图像分类和边缘检测

人工光突触为克服数据存储和处理中的冯诺依曼瓶颈,提供了一种有效的解决方案.人工光突触通过消除带宽连接密度的权衡和低功耗,展现了其相较于电突触的优势.钙钛矿量子点由于其易于合成和良好的光电性能,在人工光突触中引起了广泛的关注.然而,有限的载流子迁移率和非线性性能阻碍了它在神经形态中的应用.本研究提出了一种吸附CsPbBr_(3)的MXene纳米结构(CsPbBr_(3)-MXene),即在MXene纳米片上原位生长CsPbBr_(3)量子点,并将其作为光电突触晶体管的吸光层,CsPbBr_(3)和MXene形成的异质结构增强了光电流的产生.在相同的光脉冲刺激下,与仅含CsPbBr_(3)的突触晶体管相比,CsPbBr_(3)-MXene突触晶体管的兴奋性突触后电流(EPSC)提高了24.6%.经过计算和比较,其线性度有了明显的改善(从4.586到1.099);此外,其对手写数字分类的识别准确率也显著提高(从86.13%到92.05%);边缘检测的F1分数也有所提高(从0.8165到0.9065),更加接近于1.这些提升表明这项工作将有助于神经计算领域的进一步发展.

A Novel Gait Pattern Recognition Method Based on LSTM-CNN for Lower Limb Exoskeleton

This paper describes a novel gait pattern recognition method based on Long Short-Term Memory(LSTM)and Convolutional Neural Network(CNN)for lower limb exoskeleton.The Inertial Measurement Unit(IMU)installed on the exoskeleton to collect motion information,which is used for LSTM-CNN input.This article considers five common gait patterns,including walking,going up stairs,going down stairs,sitting down,and standing up.In the LSTM-CNN model,the LSTM layer is used to process temporal sequences and the CNN layer is used to extract features.To optimize the deep neural network structure proposed in this paper,some hyperparameter selection experiments were carried out.In addition,to verify the superiority of the proposed recognition method,the method is compared with several common methods such as LSTM,CNN and SVM.The results show that the average recognition accuracy can reach 97.78%,which has a good recognition eff ect.Finally,according to the experimental results of gait pattern switching,the proposed method can identify the switching gait pattern in time,which shows that it has good real-time performance.

Optimal Feature Extraction Using Greedy Approach for Random Image Components and Subspace Approach in Face Recognition

An innovative and uniform framework based on a combination of Gabor wavelets with principal component analysis （PCA） and multiple discriminant analysis （MDA） is presented in this paper. In this framework, features are extracted from the optimal random image components using greedy approach. These feature vectors are then projected to subspaces for dimensionality reduction which is used for solving linear problems. The design of Gabor filters, PCA and MDA are crucial processes used for facial feature extraction. The FERET, ORL and YALE face databases are used to generate the results. Experiments show that optimal random image component selection （ORICS） plus MDA outperforms ORICS and subspace projection approach such as ORICS plus PCA. Our method achieves 96.25%, 99.44% and 100% recognition accuracy on the FERET, ORL and YALE databases for 30% training respectively. This is a considerably improved performance compared with other standard methodologies described in the literature.

Speaker-independent speech emotion recognition by fusion of functional and accompanying paralanguage features

Functional paralanguage includes considerable emotion information, and it is insensitive to speaker changes. To improve the emotion recognition accuracy under the condition of speaker-independence, a fusion method combining the functional paralanguage features with the accompanying paralanguage features is proposed for the speaker-independent speech emotion recognition. Using this method, the functional paralanguages, such as laughter, cry, and sigh, are used to assist speech emotion recognition. The contributions of our work are threefold. First, one emotional speech database including six kinds of functional paralanguage and six typical emotions were recorded by our research group. Second, the functional paralanguage is put forward to recognize the speech emotions combined with the accompanying paralanguage features. Third, a fusion algorithm based on confidences and probabilities is proposed to combine the functional paralanguage features with the accompanying paralanguage features for speech emotion recognition. We evaluate the usefulness of the functional paralanguage features and the fusion algorithm in terms of precision, recall, and F1-measurement on the emotional speech database recorded by our research group. The overall recognition accuracy achieved for six emotions is over 67% in the speaker-independent condition using the functional paralanguage features.

Improved Anchored Neighborhood Regression Enhancement for Face Recognition

Although progress in face recognition is encouraging, the accuracy rate of face recognition remains to be increased. Since the face image quality has a positive influence on face recognition accuracy, the image enhancement methods are popular in face recognition. Most current image enhancement methods aim at improving visual appearance, but cannot improve recognition accuracy remarkably. In this paper, a feature evaluation operator is designed to overcome this problem. The operator selects patches with the best quality, and then face image is reconstructed with the selected patches. The proposed algorithm is tested on two different face recognition applications. Accuracy is raised after enhancement, and the result proves that the proposed algorithm is effective.

基于肖特基势垒调控的低能耗高识别精度的有机突触晶体管

为了构建类脑神经形态计算网络,单一的人工突触器件应该表现出极低的能量消耗,达到飞焦耳级别.然而,大多数现有的基于欧姆接触的低能耗突触器件实施方案,要么结构复杂,要么需要特定材料,这些因素都阻碍了人工神经网络的进一步发展.本文报告了一种肖特基势垒调控的有机突触晶体管(SBROST).通过在源电极和半导体之间的接触界面引入肖特基势垒,显著降低了单个突触事件的能耗,与使用欧姆接触的传统有机突触晶体管相比,SBROST的性能得到了改善.SBROST不仅可在低工作电压和电流下运行,还具有可适用于不同有机突触器件的简单结构.此外,SBROST可以实现低能耗下的高识别精度.经过100个周期,基于SBROST的手写人工神经网络表现出卓越的识别精度(93.53%),接近理想精度(95.62%).将肖特基势垒引入突触晶体管的方案为构建类脑神经计算网络提供了新的视角.

CsPbBr_(3)quantum dots/PDVT-10 conjugated polymer hybrid film-based photonic synaptic transistors toward high-efficiency neuromorphic computing

Photonic synaptic transistors are promising neuromorphic computing systems that are expected to circumvent the intrinsic limitations of von Neumann-based computation.The design and construction of photonic synaptic transistors with a facile fabrication process and highefficiency information processing ability are highly desired,while it remains a tremendous challenge.Herein,a new approach based on spin coating of a blend of CsPbBr_(3) perovskite quantum dot(QD)and PDVT-10 conjugated polymer is reported for the fabrication of photonic synaptic transistors.The combination of flat surface,outstanding optical absorption,and remarkable charge transporting performance contributes to high-efficiency photon-to-electron conversion for such perovskite-based synapses.High-performance photonic synaptic transistors are thus fabricated with essential synaptic functionalities,including excitatory postsynaptic current(EPSC),paired-pulse facilitation(PPF),and long-term memory.By utilizing the photonic potentiation and electrical depression features,perovskite-based photonic synaptic transistors are also explored for neuromorphic computing simulations,showing high pattern recognition accuracy of up to 89.98%,which is one of the best values reported so far for synaptic transistors used in pattern recognition.This work provides an effective and convenient pathway for fabricating perovskite-based neuromorphic systems with high pattern recognition accuracy.

Fault Diagnosis of Photovoltaic Array Based on Deep Belief Network Optimized by Genetic Algorithm

When using deep belief networks(DBN)to establish a fault diagnosis model,the objective function easily falls into a local optimum during the learning and training process due to random initialization of the DBN network bias and weights,thereby affecting the computational efficiency.To address the problem,a fault diagnosis method based on a deep belief network optimized by genetic algorithm(GA-DBN)is proposed.The method uses the restricted Boltzmann machine reconstruction error to structure the fitness function,and uses the genetic algorithm to optimize the network bias and weight,thus improving the network accuracy and convergence speed.In the experiment,the performance of the model is analyzed from the aspects of reconstruction error,classification accuracy,and time-consuming size.The results are compared with those of back propagation optimized by the genetic algorithm,support vector machines,and DBN.It shows that the proposed method improves the generalization ability of traditional DBN,and has higher recognition accuracy of photovoltaic array faults.

Simultaneous Identification of Vehicular Parameters and Structural Damages in Bridge

In this paper, we present a method for simultaneously identifying the vehicular parameters and the structural damage of bridges. By using the dynamic response data of bridge in coupled vibration state and the algorithm for the inverse problem, the vehicle-bridge coupling model is built through combining the motion equations of both vehicle and the bridge based on their interaction force relationship at contact point. Load shape function method and Newmark iterative method are used to solve the vibration response of the coupled system. Penalty function method and regularization method are interchangeable in the process until the error is less than the allowable value. The proposed method is applied on a single-span girders bridge, and the recognition results verify the feasibility, high accuracy and robustness of the method.