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Quaternary quadratic lattices over number fields

INTERNATIONAL JOURNAL OF NUMBER THEORY 2019年第2期15卷 309-325页

作者： Kirschmer, Markus Nebe, Gabriele Rhein Westfal TH Aachen Lehrstuhl Math D D-52056 Aachen Germany

We relate proper isometry classes of maximal lattices in a totally definite quaternary quadratic space (V, q) with trivial discriminant to certain equivalence classes of ideals in the quaternion algebra representing the Clifford invariant of (V, q). This yields a good algorithm to enumerate a system of representatives of proper isometry classes of lattices in genera of maximal lattices in (V, q).

关键词： Quaternary quadratic forms lattices over totally real fields genera of lattices orders in quaternion algebras class numbers classification algorithm

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Diagnosis of Burkitt lymphoma in due time:: a practical approach

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BRITISH JOURNAL OF HAEMATOLOGY 2006年第3期134卷 294-301页

作者： Cogliatti, SB Novak, U Henz, S Schmid, U Möller, P Barth, TFE Kantonsspital Dept Pathol CH-9007 St Gallen Switzerland Columbia Univ Inst Canc Genet New York NY USA Kantonsspital Dept Internal Med CH-9007 St Gallen Switzerland Univ Hosp Ulm Dept Pathol Ulm Germany

The quick diagnosis of Burkitt lymphoma (BL) and its clear-cut differentiation from diffuse large B-cell lymphoma (DLBCL) is of great clinical importance because treatment strategies for these two disease entities differ markedly. As these two lymphomas are difficult to distinguish using the current World Health Organization classification, we studied 39 cases of highly proliferative peripheral blastic B-cell lymphoma (HPBCL) to establish a practical differential-diagnostic algorithm. Characteristics set for BL were a typical morphology, a mature B-cell phenotype of CD10(+), Bcl-6(+) and Bcl-2(-) tumour cells, a proliferation rate of > 95%, and the presence of C-MYC rearrangements in the absence of t(14;18)(q32;q21). Altogether, these characteristics were found in only five of 39 cases, whereas the majority of tumours revealed mosaic features. We then followed a pragmatic stepwise approach for a classification algorithm that included the assessment of C-MYC status to stratify HPBCL into four predefined diagnostic categories (DC), namely DC I (5/39, 12.8%): 'classical BL', DC II (11/39, 28.2%): 'atypical BL', DC III (9/39, 23.1%): 'C-MYC+ DLBCL' and DC IV (14/39, 35.9%): 'C-MYC- HPBCL'. This proposal may serve as a robust and objective operational basis for therapeutic decisions for HPBCL within 1 week and is applicable to be evaluated for its prognostic relevance in clinical trials with uniformly treated patients.

关键词： routine diagnostics Burkitt lymphoma diffuse large B-cell lymphoma classification algorithm C-MYC rearrangement

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Sensors-Based Ambient Assistant Living via E-Monitoring Technology

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Computers, Materials & Continua 2022年第12期73卷 4935-4952页

作者： Sadaf Hafeez Yazeed Yasin Ghadi Mohammed Alarfaj Tamara al Shloul Ahmad Jalal Shaharyar Kama Dong-Seong Kim Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Electrical Engineering King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of IT Convergence Engineering Kumoh National Institute of TechnologyGumiKorea

Independent human living systems require smart,intelligent,and sustainable online monitoring so that an individual can be assisted *** from ambient assisted living,the task of monitoring human activities plays an important role in different fields including virtual reality,surveillance security,and human interaction with *** systems have been developed in the past with the use of various wearable inertial sensors and depth cameras to capture the human *** this paper,we propose multiple methods such as random occupancy pattern,spatio temporal cloud,waypoint trajectory,Hilbert transform,Walsh Hadamard transform and bone pair descriptors to extract optimal features corresponding to different human *** features sets are then normalized using min-max normalization and optimized using the Fuzzy optimization ***,the Masi entropy classifier is applied for action recognition and *** have been performed on three challenging datasets,namely,UTDMHAD,50 Salad,and *** experimental evaluation,the proposed novel approach of recognizing human actions has achieved an accuracy rate of 90.1%with UTD-MHAD dataset,90.6%with 50 Salad dataset,and 89.5%with CMU-MMAC *** experimental results validated the proposed system.

关键词： classification algorithm human action recognition motion sensors machine learning Masi entropy

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Rotation-Tolerant Wireless Power Transmission Scheme with Smart Positioning for Cognitive Research on Moving Animals

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IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2024年第1期18卷 123-130页

作者： Pahlavan, Saeideh Ashtiani, Shahin Jafarabadi Univ Tehran Coll Engn Sch Elect & Comp Engn Tehran 14395515 Iran

This article presents a novel wireless power transmission scheme designed for moving loads. Specifically focused on compensating for the tilt misalignment of the receiver. By utilizing phase-shifted excitation signals from an array of transmitters, the proposed scheme effectively mitigates the impact of misalignment, locally. The application of this scheme holds particular relevance for studying the behavior of moving animals in cognitive research. The system incorporates a cage with two transmitter arrays positioned on the top and bottom sides. To smart determining the receiver's position, the proposed structure utilizes current feedback from the driving circuits and employs SVM (Support Vector Machine) classification algorithms for positioning. Furthermore, when the receiver coil is tilted, a phase shift mechanism significantly enhances the power delivered to the receiver. Additionally, the use of an overlapped transmitter array enhances rotation tolerance and improves the uniformity of the magnetic fields for moving objects. The performance of the proposed scheme is validated through extensive simulations and measurements using a fabricated prototype. Notably, the designed system achieves a power delivery of 296 mW to the load at a 90 degrees angular misalignment, compared to 1.67 mu W delivered by conventional array system.

关键词： classification algorithm free-positioning phase shift transmitter array wireless power transfer

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Advancements and Challenges in Non-Invasive Electrocardiography for Prenatal, Intrapartum, and Postnatal Care: A Comprehensive Review

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IEEE ACCESS 2024年 12卷 44730-44747页

作者： Marnani, Roya Aghadavoud Jaros, Rene Pavlicek, Jan Martinek, Radek Kahankova, Radana Vilimkova VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Dept Cybernet & Biomed Engn Ostrava 70800 Czech Republic Univ Hosp Ostrava Dept Pediat Ostrava 70800 Czech Republic Ostrava Univ Fac Med Ostrava 70800 Czech Republic

Non-invasive electrocardiography (NI-ECG) has become an indispensable tool for monitoring fetal and neonatal cardiac activity throughout the stages of pregnancy and postpartum care. This review emphasizes the distinct advantages of NI-ECG, including extended monitoring capabilities and valuable insights into fetal and neonatal health. The exploration of textile electrodes is highlighted as a promising alternative, offering improved comfort and reduced skin irritation compared to traditional adhesive electrodes. However, challenges in NI-ECG persist, with electrode placement, quantity, and noise removal being key considerations. The review underscores the significance of addressing interference sources, such as maternal and fetal body signals, motion artifacts, and electrode-skin contact. Additionally, the discussion extends to computer-aided diagnostics, presenting novel approaches for classifying fetal and neonatal health during pregnancy and delivery. Ongoing research aims to optimize electrode placement, develop advanced noise reduction algorithms, and explore sophisticated classification methodologies. These advancements hold the potential to enhance electronic monitoring, enabling early detection of abnormalities and promoting improved outcomes in prenatal and neonatal care.

关键词： Pediatrics Electrocardiography Electrodes Fetal heart rate Fetus Biomedical monitoring Abdomen Patient monitoring Neonatology Noninvasive treatment Biomedical signal processing Deep learning Machine learning Artificial intelligence classification algorithms Abdominal monitoring fetal electrocardiography fetal heart rate monitoring neonatal electrocardiography non-invasive monitoring signal processing textile electrodes artificial intelligence classification algorithm deep learning machine learning

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Application of K-Shell algorithm in Key Point Identification and Cascading Failure in Urban Public Safety Transportation Network

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IEEE ACCESS 2024年 12卷 32152-32163页

作者： Xiao, You Ren, Yuan Wuhan Univ Sci & Technol Sch Law & Econ Wuhan 430065 Peoples R China Wuhan Univ Sci & Technol Inst Adv Studies Humanities & Social Sci Wuhan 430065 Peoples R China

The rapid development of modern urban public transportation and people's demand for efficient transportation have made intelligent management of urban public transportation networks a hot research field. In view of this, a composite complex traffic network node classification algorithm model is established based on the shell decomposition algorithm combined with a cascading failure model. The final research findings denote that the classification refinement level of the comprehensive shell decomposition value is about 50% higher than that of the WK-shell decomposition value. The comprehensive shell decomposition algorithm, which integrates global and local influence factors, can almost achieve one shell decomposition value per node in classification. The number of layers with nodes below 30 in the actual transportation network dataset test accounts for about 98% of the total number under the improved comprehensive shell decomposition algorithm, and roughly 50% under the composite shell decomposition algorithm, indicating that the accuracy of the former algorithm is higher. When the relationship ratio in robustness testing is 1:1:1, the average comprehensive shell decomposition value is at a higher position, which is about 3% higher than the WK-shell decomposition value and about 5% higher than the traditional shell decomposition value. In addition, when the proportions are 5:1:1, 1:5:1, and 1:1:5, the failure points tested under the comprehensive shell decomposition value are still the most, indicating that the comprehensive shell decomposition value considers more factors, is more cautious, and is closer to the actual situation. Through experimental data, the proposed composite complex traffic network node classification algorithm model, which integrates improved shell decomposition algorithm and cascading failure model, has certain practicality and feasibility.

关键词： classification algorithms Analytical models Neural networks Mathematical models Predictive models Fault diagnosis Public transportation Intelligent systems Management Urban areas Traffic control Intelligent transportation systems Pattern analysis intelligent management shell decomposition algorithm cascading failure model classification algorithm

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Recognition of air-conditioner operation from indoor air temperature and relative humidity by a data mining approach

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ENERGY AND BUILDINGS 2016年 111卷 233-241页

作者： Zhou, Hao Qiao, Lifeng Jiang, Yi Sun, Hejiang Chen, Qingyan Tianjin Univ Sch Environm Sci & Engn Tianjin Key Lab Indoor Air Environm Qual Control Tianjin 300072 Peoples R China Intelligent Green Solut Ltd Beijing 100084 Peoples R China Purdue Univ Sch Mech Engn W Lafayette IN 47907 USA

The variety of occupant behaviors in buildings have led to a significant mismatch between simulated building energy performance and measured one. It is crucial to collect real occupant behaviors in buildings to achieve accurate simulation purpose, although there exists a great challenge due to the cost of monitoring devices and privacy concerns. This study proposed an inexpensive and minimally intrusive method, to recognize behavior information from environment parameters by data mining approach. To validate this method, experiments were conducted in three bedrooms. Two types of classification algorithms were developed to recognize AC operations from the experiment data of indoor air temperature and relative humidity. Two types of recognition rules were generated from algorithm training in one dataset, and tested in the other datasets. Based on the testing results, the performance of the two algorithms were evaluated and compared. The results indicated that the C4.5 decision tree algorithm was not suitable for mining AC operations, while the curve description algorithm had good performance in processing the time-series curves of air temperature and relative humidity. Through this experiment, it is confirmed that AC operations can be recognized from indoor air temperature and relative humidity by data mining approach. The main contribution of this study is that a promising approach was developed, which is inexpensive and minimally intrusive on gathering and interpreting information about occupants' daily behaviors. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Occupant behavior Air-conditioner operation Data mining classification algorithm C4.5 decision tree Curve description Time-series curve

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Oil source recognition technology using concentration-synchronous-matrix-fluorescence spectroscopy combined with 2D wavelet packet and probabilistic neural network

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SCIENCE OF THE TOTAL ENVIRONMENT 2018年 616卷 632-638页

作者： Huang, Xiao-Dong Wang, Chun-Yan Fan, Xin-Min Zhang, Jin-Liang Yang, Chun Wang, Zhen-Di Weifang Univ Dept Phys & Elect Sci Weifang 261061 Peoples R China Weifang Univ Inst New Electromagnet Mat Weifang 261061 Peoples R China Beijing Normal Univ Coll Resources Sci & Technol Beijing 100875 Peoples R China Environm Canada Sci & Technol Branch ESTS 335 River Rd Ottawa ON K1A 0H3 Canada

Developing an accurate, rapid and economic oil source recognition method is essential for water recourses protection. Concentration-synchronous-matrix-fluorescence (CSMF) spectroscopy combined with 2D wavelet packet and probabilistic neural network (PNN) was proposed for source recognition of crude oil and petroleum products samples in this study. 2D wavelet packet was used to extract wavelet packet coefficients as the feature vectors from CSMF contour image and four algorithms, Back-propagation (BP) neural network, Radial based function neural network (RBFNN), Support vector Machine (SVM) and probabilistic neural network (PNN) were carried out for pattern recognition. With the introduction of interference factors such as weathering and sea water adulteration to the three samples from Bohai bay territory of China, the comparison about accuracy and recognition time of the four methods was discussed and the results showed that PNN network maintain the highest recognition accuracy and speed. These findings may offer potential application for oil spill recognition for unconventional oil. (c) 2017 Elsevier B.V. All rights reserved.

关键词： Oil spill Oil source recognition Concentration-synchronous-matrixfluorescence (CSMF) classification algorithm Weathering experiment

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Using Pattern Position Distribution for Software Failure Detection

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2013年第2期6卷 234-243页

作者： Li, Chunping Chen, Ziniu Du, Hao Wang, Hui Wilkie, George Augusto, Juan C. Liu, Jun Tsinghua Univ Sch Software Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China Univ Ulster Fac Comp & Engn Jordanstown BT37 0QB North Ireland

We present a novel approach for using the pattern position distribution as features to detect software failure. In this approach, we divide an execution sequence into several sections and compute the pattern distribution in each section. The distribution of all patterns is then used as features to train a classifier. This approach outperforms conventional frequency based methods by more effectively identifying software failures occurring through misused software patterns. Comparative experiments show the effectiveness of our approach.

关键词： Sequential Patterns classification algorithm Software Failure Anomaly Detection

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Load level forwarding work element analysis based on automatic follow-up data

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SILVA FENNICA 2016年第3期50卷

作者： Manner, Jussi Palmroth, Lauri Nordfjell, Tomas Lindroos, Ola Forestry Res Inst Sweden Skogforsk Uppsala Sci Pk SE-75183 Uppsala Sweden John Deere Forestry POB 472 FI-33101 Tampere Finland Swedish Univ Agr Sci Dept Forest Biomat & Technol SE-90183 Umea Sweden

Recent developments in on-board technology have enabled automatic collection of follow-up data on forwarder work. The objective of this study was to exploit this possibility to obtain highly representative information on time consumption of specific work elements (including overlapping crane work and driving), with one load as unit of observation, for large forwarders in final felling operations. The data used were collected by the John Deere TimberLink system as nine operators forwarded 8868 loads, in total, at sites in mid-Sweden. Load-sizes were not available. For the average and median extraction distances (219 and 174 m, respectively), Loading, Unloading, Driving empty, Driving loaded and Other time effective work (PM) accounted for ca. 45, 19, 8.5, 7.5 and 14% of total forwarding time consumption, respectively. The average and median total time consumptions were 45.8 and 42.1 minutes/load, respectively. The developed models explained large proportions of the variation of time consumption for the work elements Driving empty and Driving loaded, but minor proportions for the work elements Loading and Unloading. Based on the means, the crane was used during 74.8% of Loading PM time, the driving speed was nonzero during 31.9% of the Loading PM time, and Simultaneous crane work and driving occurred during 6.7% of the Loading PM time. Time consumption per load was more strongly associated with Loading drive distance than with extraction distance, indicating that the relevance of extraction distance as a main indicator of forwarding productivity should be re-considered.

关键词： haulage cut-to-length logging harvesting data gathering automatic recording classification algorithm Hidden Markov Models

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