Similarity Comparison Algorithm Research Articles

A Self-Organizing Multi-Layer Agent Computing System for Behavioral Clustering Recognition

Video behavior recognition often needs to focus on object motion processes. In this work, a self-organizing computational system oriented toward behavioral clustering recognition is proposed, which achieves the extraction of motion change patterns through binary encoding and completes motion pattern summarization using a similarity comparison algorithm. Furthermore, in the face of unknown behavioral video data, a self-organizing structure with layer-by-layer accuracy progression is used to achieve motion law summarization using a multi-layer agent design approach. Finally, the real-time feasibility is verified in the prototype system using real scenes to provide a new feasible solution for unsupervised behavior recognition and space-time scenes.

A novel smart photoelectric lock system: Speech transmitted by laser and speech to text

We propose a circuit that modulates a speech signal to a laser, using which the speech signal can be transmitted using the laser. Also, it shows the use of a platform based on embedded ARM (Advanced RISC Machine), running a small deep learning model based on TDNN (Time delay neural network) and LSTM (Long short-term memory), and converting speech to text, and use the text cipher for unlocking. This research implements a smart lock system that can set a pre-record speech cipher and verify the similarity through a laser transmission speech cipher to unlock it. In our experiment result, the English speech of laser transmission can reach a WER (Word error rate) of 14.06% through the deep learning model to recognize the content of the speech cipher. We also design a similarity comparison algorithm based on LCS (Longest common subsequence) to compare the character set of the laser transmission speech compare and the prerecord speech cipher to calculate the similarity rate. Through the similarity comparison algorithm, when the WER is 27.27%, the male speech samples used in this study still have a 95% unlocking success rate, while the female speech samples have a 100% unlocking success rate. Compared with only using automatic speech recognition (ASR) to unlock, the method we propose is to compare the similarity of the content of speech cipher. The method significantly improves the unlocking fault tolerance of using lasers to transmit audio. Therefore, by using the laser to transmit the speech cipher, the usability of the photoelectric smart lock system has been significantly improved. At the same time, the characteristics of the laser are not easy to eavesdrop on the cipher, which can also improve security.

Herbal Compound Screening with GPU Computation on ZINC Database through Similarity Comparison Approach

Covid-19 is a global pandemic that drives many researchers to strive to look for its solution, especially in the field of health, medicine, and total countermeasures. Early screening with in-silico processes is crucial to minimize the search space of the potential drugs to cure a disease. This research aims to find potential drugs of covid-19 disease in the ZINC database to be further investigated through the in-vitro method. About 997.402.117 chemical compounds are searched about their similarity to some of the confirmed drugs to combat coronavirus. Sequential computation would take months to accomplish this task. The general programming graphic processing unit approach is used to implement a similarity comparison algorithm in parallel, in order to speed up the process. The result of this study shows the parallel algorithm implementation can speed up the computation process up to 55 times faster, and also that some of the chemical compounds have high similarity scores and can be found in nature

A Method for Detection of Rare Industrial Fake Film Based on Deep Learning

In order to solve the industrial fake film problem produced by repeated imaging in the field of nondestructive testing, this paper proposes an image similarity comparison algorithm of X-ray film based on the Siamese neural network. Firstly, in order to accurately locate and detect weld areas from high-resolution X-ray film, a robust location and detection algorithm based on the Gaussian function is proposed. Next, in order to solve the problem that the deep learning model system extracts features according to squares by default, which results in the disappearance of film features with high horizontal and vertical features, effective weld regions are used and processed in blocks. Then, a small sample data set for weld similarity discrimination is established by using each block region, and an industrial fake film discrimination model based on twin neural networks is constructed. Finally, the corresponding weights are set for different fake feature information areas, and a similarity comparison system for industrial fake film detection is proposed. The experiment shows that the method in this paper can not only accurately locate the weld area but also accurately detect the fake film in the industry, avoiding the influence of the geometric transformation of the image and the insufficient number of data sets in the process of similarity comparison, and has a good detection effect.

HMM‐TCN‐based health assessment and state prediction for robot mechanical axis

Aiming at the problems of high manual cost, low efficiency, and low precision of the mechanical axis health management in industrial robot applications, this paper proposes a health assessment and state prediction algorithm based on hidden Markov model (HMM) and temporal convolutional networks (TCN). First, the MPdist similarity comparison algorithm is used to construct the mechanical axis health index. Then the hidden Markov model is trained with observable sensor data. After that, the temporal convolution neural network is used to predict state transition time iteratively, and the predicted results are decoded by HMM. The experimental results show that the HMM-TCN model can accurately assess the health state of the mechanical axis and predict the state transition in real-time. The prediction accuracy of this method reaches 87.5%, and the error interval locates in [−3,9] time steps. The accuracy, early/late prediction indicators are better than HMM-RNN, HMM-LSTM, and HMM-GRU.

A Low-Latency Object Detection Algorithm for the Edge Devices of IoV Systems

The emergence of edge computing (EC) and intelligent vision-based driver assistance system is of great significance for the prospective development of Internet of Vehicle (IoV). The additional computation capability and extensive network coverage provides energy-limited smart devices with more opportunities to enable IoV system for time-sensitive applications. However, when implemented in a vision-based driver assistance system, the transmission of a large amount of redundant data not only causes delay but also severely compromises the accuracy of object detection. In this paper, an improved object detection algorithm based on video key-frame for latency reduction on edge IoV system is proposed. It can significantly improve latency reduction performance at the expense of small detection accuracy. In our proposal, we adopt an important coefficient and frame similarity comparison algorithm to filter redundant frames and achieve key frames for object detection. Then an improved Haar-like feature based classification algorithm is used for object detection under the edge computation model. Finally, a scalable cluster object detection system is implemented as a practical EC case to verify our proposal, and extensive simulations confirm the superiority of the proposed scheme over regular schemes. It can speed up about 84 times with 40% of the similar frames filtered in comparison.

Characterization and reconstruction for stochastically distributed void morphology in nano‑silver sintered joints

Driven by sintering kinetics, stochastically distributed voids exist inevitably in nano‑silver sintered joints to affect their electrical, mechanical and thermal properties, reliability, etc. In this paper, a holistic approach including both void morphology characterization and reconstruction stages is developed for the reconstruction of Stochastically Distributed Void Morphologies (SDVMs) in nano‑silver sintered joints. A set of dispersion parameters, including area A, distance l, angle α, and aspect ratio δ, are firstly proposed for quantitatively characterizing the voids dispersion features of the SDVMs. Then on the basis of the characterization results, a reconstruction approach is developed based on the random medium theory and similarity comparison algorithm. The applicability and validity of the proposed voids dispersion characterization and SDVM reconstruction methods are verified through numerical and experimental case studies, respectively. And effects on the dispersion features of the SDVMs by such as autocorrelation function parameters and the sintering pressure are also investigated via the comparative discussions on the cumulative distribution function (cdf) curves of all dispersion parameters.

The Similarity Comparison Algorithm of Free-Form Surfaces with Single Curvature Feature

This paper studies the shape similarity evaluation of free-form surfaces expressed by B-spline with single curvature feature and proposes a similarity evaluation algorithm based on curvature feature. Firstly, we calculate the normal vector direction of the two surfaces compared, and use it as the Z axis, so that the two surfaces are aligned on the Z axis. Then, the two surfaces are cut with planes that all perpendicular to the Z axis, and the intersection sets of two surfaces are obtained respectively. Finally, we design the similarity algorithm of plane curves to realize the similarity comparison of corresponding curves in the two sets of intersection, and which is used as the basis for evaluating the similarity between two surfaces. The algorithm transforms the problem of similarity comparison between 3D surfaces into two dimensional space by plane cutting method, and reduces the complexity of the problem effectively. The algorithm only needs to align one coordinate axis in the process of posture adjustment, so it is easy to implement. In order to test the effect of the algorithm, simulation experiments on different type of single curvature feature B-spline surfaces are carried out. The results show that the proposed similarity comparison algorithm of free-form surfaces is feasible and effective.

Security Optimization of Wireless Sensor Networks Based on Cloud Platform

In order to improve the security of wireless sensor network, the trust model of wireless sensor network based on cloud theory is established. The similarity comparison algorithm is introduced. Trust is expressed as a quantitative value. The new update function is designed to make the acquisition of trust more reasonable. In the research method and algorithm design, the wireless sensor network is different from the traditional network security. The balance problem of wireless sensor network security in algorithm computing strength and security intensity is studied. The results show that the trust model has good robustness. It can identify the malicious nodes accurately and quickly, and prevent the network from being destroyed. Therefore, the idea of cloud theory can effectively improve the security of the network.

Subjective trust metric based on weighted multi-attribute cloud

The existing trust metrics based on the cloud model lack of the multi-granularity and timeliness consideration.For this reason, a trust metric algorithm based on weighted multi-attribute cloud was proposed. First of all, multi-attribute trust cloud on trust metric was used to refine the grain size, and time decay function was introduced in the entity trust computing; second, multi-attribute comprehensive and multi-path merge was used to get entity ultimate trust cloud. Finally,the trust level of the entity was obtained by comparison with basis trust cloud using cloud similarity comparison algorithm. The simulation results under grid computing environment show that when the node interaction reached 100 times, the interaction success rate of weighted multi-attribute cloud metric was 80%, significantly higher than 65% of the traditional method. The simulation results show that the cloud using the weighted multi-attribute trust cloud metric measurement method can improve the accuracy of trust metric.

Dependency-Based Malware Similarity Comparison Method

Malware similarity comparison is one of the basic works in malware analysis and detection. Presently, most similarity comparison methods treat malware as CFG, or behavior sequences. Malware writers use obfuscation, packers and other means of technique to confuse traditional similarity comparison methods. This paper proposes a new approach in identifyling the similarities between malware samples, which rely on control dependence and data dependence. First, the dynamic taint analysis is performed to obtain control dependence relations and data dependence relations. Next, a control dependence graph and data dependence graph are constructed. Similarity information is obtained by comparing these two types of graph. In order to take full advantage of the inherent behavior of malicious codes and to increase the accuracy of comparison and anti-jamming capability, the loops are recued and the rubbish is removed by means of the dependence graph pre-processing, which reduces the complexity of the similarity comparison algorithm and improves the performance of the algorithm. The proposed prototype system has been applied to wild malware collections. The results show that the accuracy of the method and comparison capabilities all have an obvious advantage.

A novel algorithm for satellite data transmission

For remote sensing satellite data transmission, a novel algorithm is proposed in this paper. It integrates different type feature descriptors into multistage recognizers. In the first level, the dynamic clustering algorithm is used. In the second level, the improved support vector machines algorithm demonstrates its validity. In the third level, the shape matrices similarity comparison algorithm shows its excellent performance. The single child recognizers are connected in series, but they are independent of each other. Objects which are not recognized correctly by the lower level recognizers are then put into the higher level recognizers. Experimental results show that the multistage recognition algorithm improves the accuracy greatly with higher level feature descriptors and higher level recognizers. The algorithm may offer a new methodology for high speed satellite data transmission.