Recognition of Taxi Violations Based on Semantic Segmentation of PSPNet and Improved YOLOv3

Taxi has the characteristics of strong mobility and wide dispersion, which makes it difficult for relevant law enforcement officers to make accurate judgment on their illegal acts quickly and accurately. With the investment of intelligent transportation system, image analysis technology has become a new method to determine the illegal behavior of taxis, but the current image analysis method is still difficult to support the detection of illegal behavior of taxis in the actual complex image scene. To solve this problem, this study proposed a method of taxi violation recognition based on semantic segmentation of PSPNet and improved YOLOv3. (1) Based on YOLOv3, the proposed method introduces spatial pyramid pooling (SPP) for taxi recognition, which can convert vehicle feature images with different resolutions into feature vectors with the same dimension as the full connection layer and solve the problem of repeated extraction of YOLOv3 vehicle image features. (2) This method can recognize two different violations of taxi (blocking license plate and illegal parking) rather than only one. (3) Based on PSPNet semantic segmentation network, a taxi illegal parking detection method is proposed. This method can collect the global information of road condition images and aggregate the image information of different regions, so as to improve the ability to obtain the global information orderly and improve the accuracy of taxi illegal parking detection. The experimental results show that the proposed method has excellent recognition performance for the detection rate of license plate occlusion behavior DR is 85.3%, and the detection rate of taxi illegal parking phenomenon DR is 96.1%.

Tailings Settlement Velocity Identification Based on Unsupervised Learning

In order to reasonably and accurately acquire the settlement interface and velocity of tailings, an identification model of tailing settlement velocity, based on gray images of the settlement process and unsupervised learning, is constructed. Unsupervised learning is used to classify stabilized tailing mortar, and the gray value range of overflow water is determined. Through the identification of overflow water in the settlement process, the interface can be determined, and the settlement velocity of tailings can be calculated. Taking the tailings from a copper mine as an example, the identification of tailings settling velocity was determined. The results show that the identification model of tailing settlement speed based on unsupervised learning can identify the settlement interface, which cannot be manually determined in the initial stage of settlement, effectively avoiding the subjectivity and randomness of manual identification, and provide a more scientific and accurate judgment. For interfaces that can be manually recognized, the model has high recognition accuracy, has a rapid and efficient recognition process, and the relative error can be controlled within 3%. It can be used as a new technology for measuring the settling velocity of tailings.

Strategy and Technique of High Efficiency Balancing in Field for Turbo-Generator Units with Large Capacity

Abstract.This paper aims at the high efficiency of field balancing for turbo-generator with large capacity currently, and introduces the strategies and key techniques of the rotor system balancing with practical cases of power plant in field. The acquisition, analysis and former processing of the original vibration data for balance calculation are included. Furthermore, they involve complete measuring points and conditions, accurate judgment for the types of unbalance exciting force and selection of stable vibration data, all these could reduce the blindness of balancing effectively. The strategies and techniques also contain the determination for axial plane of unbalance by the modal method, then the optimal steps and the plane of adding weight are chosen during the implementation of balancing. Besides, this paper also introduces the analysis and selection of influence coefficients and the phase of trial weight, these can help determine the final correction weight accurately in order to guarantee the balancing process prompt and efficient. Meanwhile, the restriction of practical location for adding weight and construction period of maintenance and production for the units should be considered during the high efficiency balancing in field. These strategies and techniques of high efficiency balancing have practical application value in promoting the technology of field balancing for turbo- generator units with large capacity.

Traffic Problems of Linjiao’s Turning Passage in Lhasa City from the Perspective of Traffic Sociology

Objectives: The transportation problem of Linjiao transit passage in Lhasa City from the perspective of traffic sociology is studied. Methods: Firstly, the research history and current situation of experts in the field of traffic congestion prediction are studied. The common parameters and models of congestion prediction are analyzed. Results: Combined with the complexity of road traffic structure and the possession of a large number of high-dimensional traffic data records, the use of a prediction model is finally determined based on RNN-RBM deep learning network. Through the research and analysis of all-day road traffic flow data, accurate judgment and prediction of traffic congestion status are made. Conclusion: In this paper, the role of the RNN model on the time axis and the state judgment of the RBM network are used to predict the traffic congestion based on the characterization of the congestion sequence.

Mitral valve regurgitation and endocarditis triggered by a needle

Background Cardiac foreign bodies are extremely rare in clinical patients, especially when foreign bodies damage the internal structure of the heart coincidentally after they penetrate the heart. Case presentation Here, we report the case of a two-year-old girl whose heart was penetrated by a needle, which triggered mitral valve regurgitation and endocarditis. After a comprehensive inspection, accurate judgment and surgical preparation, we removed the needle and repaired her mitral valve. Fortunately, she recovered postoperatively. Conclusion From this case, we can know that when cardiac foreign bodies are suspected, ultrasound is an important inspection method. Moreover, the approaches for handling each such case are different depending on the associated injuries.

The value of serum procalcitonin in the anti-infection therapy of acute stroke patients

Objectives: To investigate the value of dynamic monitoring of serum procalcitonin (PCT) in anti-infective therapy of patients with acute stroke. Methods: This is a case control retrospective study of acute stroke patients conducted from July 2016 to October 2018, in the Department of Neurology, Affiliated Hospital of Hebei University, who who reached within twenty four hours. They, were selected as the study subjects who were divided into infection group and non-infection group according to the inclusion and exclusion criteria. The serum PCT and CRP levels were compared between the two groups at 24 hours, 48 hours and 72 hours. In order to judge the changes of PCT level and the infection of stroke patients, different kinds of antibiotics were used for corresponding treatment. Retrospective analysis of the cases that did not monitor PCT anti infective treatment before July 2016 were compared with the cases that monitored PCT to guide anti infective treatment after July 2016, and compared the efficacy of antibiotics. Results: The serum PCT level of patients in the infection group was significantly higher than that of patients in the noninfection group (P<0.001). For the patients whose PCT<0.5 ng/ml within 72 hour, anti-infective therapy was not administered. However, for those patients whose PCT<0.5 ng/ml and CRP rose significantly, WBC, body temperature and chest CT were closely monitored. For the patients whose PCT increased slightly (0.5 ng/ml<PCT<2.0 ng/ml), first-generation and second-generation cephalosporin or semisynthetic penicillin, such as mezlocillin, were administered. For the patients whose PCT increased moderately (5 ng/ml>PCT>2 ng/ml), mezlocillin/ sulbactam or ceftriaxone/ tazobactam was administered. For patients whose PCT increased significantly (PCT>5 ng/ml), carbapenem antibiotic or a combination of two antibiotics was administered. Conclusion: Dynamic detection of serum PCT concentration can make accurate judgment on the severity of bacterial infection in patients with acute stroke and guide the rational application of antibiotics. doi: https://doi.org/10.12669/pjms.37.4.3932 How to cite this:Wang H, Li Y, Li X, Wang Z. The value of serum procalcitonin in the anti-infection therapy of acute stroke patients. Pak J Med Sci. 2021;37(4):---------. doi: https://doi.org/10.12669/pjms.37.4.3932 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Image Integration Procedures in Multisensory Medical Images: A Comprehensive Survey of the state-of-the-art Paradigms

Background: Obtaining the medical history from a patient is a tedious task for doctors as it depends on a lot of factors which are difficult to keep track from a patient’s perspective. Doctors have to rely upon the technological tools to make a swift and accurate judgment about the patient’s health. Introduction: Out of many such tools, there are two special imaging modalities known as X-ray - Computed Tomography (CT) and Magnetic Resonance imaging (MRI) which are of a significant importance in the medical world assisting the diagnosis process. Method: The advancement in signal processing theory and analysis has led to design and implementation of large number of image processing and fusion algorithms. Each of these methods have evolved in terms in their terms of their computational efficiency and visual results over the years. Result: Various researches have revealed their properties in terms of their efficiency and outreach and it has been concluded that image fusion can be very suitable process that can help to compensate the drawbacks. Conclusion: In this manuscript, recent state-of-the-art techniques have been used to fuse these image modalities and established its need and importance in a more intuitive way with the help of a wide range of assessment parameters.

Elbow Dislocation With Irretrievable Rotating-rope Injury of the Forearm: Case Report and Literature Review

Concurrent dislocation of the distal radiulnar joint of the proximal elbow and proximal radius fracture with rotatory noose injury medial to the ulnar tuberosity is an extremely rare case. No such injury has been reported in the domestic or international literature. The injury we report is similar to that of the proximal fibula of the ankle locked behind the tibial tuberosity, namely Bosworth's injury. We report a case of 36 male elbow proximal distal radial ulnar radial ulnar joint rotation joints and proximal radial fracture and dislocation bowstring in ulna nodule inside, in the rapid and timely accurate diagnosis under the emergency line open reduction and accurate judgment and treatment of this injury is challenging, early diagnosis and timely treatment can get the best curative effect.

Sentence similarity evaluation using Sent2Vec and siamese neural network with parallel structure

Sentence similarity evaluation is a significant task used in machine translation, classification, and information extraction in the field of natural language processing. When two sentences are given, an accurate judgment should be made whether the meaning of the sentences is equivalent even if the words and contexts of the sentences are different. To this end, existing studies have measured the similarity of sentences by focusing on the analysis of words, morphemes, and letters. To measure sentence similarity, this study uses Sent2Vec, a sentence embedding, as well as morpheme word embedding. Vectors representing words are input to the 1-dimension convolutional neural network (1D-CNN) with various sizes of kernels and bidirectional long short-term memory (Bi-LSTM). Self-attention is applied to the features transformed through Bi-LSTM. Subsequently, vectors undergoing 1D-CNN and self-attention are converted through global max pooling and global average pooling to extract specific values, respectively. The vectors generated through the above process are concatenated to the vector generated through Sent2Vec and are represented as a single vector. The vector is input to softmax layer, and finally, the similarity between the two sentences is determined. The proposed model can improve the accuracy by up to 5.42% point compared with the conventional sentence similarity estimation models.