Relevant bibliographies by topics / LSVM

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'LSVM'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "LSVM"

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Chu, Wenbo, Donge Zhao, Baowei Liu, Bin Zhang, and Zhiguo Gui. "Research on Target Deviation Measurement of Projectile Based on Shadow Imaging Method in Laser Screen Velocity Measuring System." Sensors 20, no. 2 (January 19, 2020): 554. http://dx.doi.org/10.3390/s20020554.

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In the laser screen velocity measuring (LSVM) system, there is a deviation in the consistency of the optoelectronic response between the start light screen and the stop light screen. When the projectile passes through the light screen, the projectile’s over-target position, at which the timing pulse of the LSVM system is triggered, deviates from the actual position of the light screen (i.e., the target deviation). Therefore, it brings errors to the measurement of the projectile’s velocity, which has become a bottleneck, affecting the construction of a higher precision optoelectronic velocity measuring system. To solve this problem, this paper proposes a method based on high-speed shadow imaging to measure the projectile’s target deviation, ΔS, when the LSVM system triggers the timing pulse. The infrared pulse laser is collimated by the combination of the aspherical lens to form a parallel laser source that is used as the light source of the system. When the projectile passes through the light screen, the projectile’s over-target signal is processed by the specially designed trigger circuit. It uses the rising and falling edges of this signal to trigger the camera and pulsed laser source, respectively, to ensure that the projectile’s over-target image is adequately exposed. By capturing the images of the light screen of the LSVM system and the over-target projectile separately, this method of image edge detection was used to calculate the target deviation, and this value was used to correct the target distance of the LSVM to improve the accuracy of the measurement of the projectile’s velocity.
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Mirbagheri, Babak, and Abbas Alimohammadi. "Integration of Local and Global Support Vector Machines to Improve Urban Growth Modelling." ISPRS International Journal of Geo-Information 7, no. 9 (August 24, 2018): 347. http://dx.doi.org/10.3390/ijgi7090347.

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The use of local information for the classification and modelling of spatial variables has increased with the application of statistical and machine learning algorithms, such as support vector machines (SVMs). This study presents a new local SVM (LSVM) model that was developed to model the probability of urban development and simulate urban growth in a subregion in the southwestern suburb of the Tehran metropolitan area, Iran, for the periods of 1992–1996 and 1996–2002. Based on the focal training sample, the model was calibrated using the cross-validation method, and the optimal bandwidth was determined. The results were compared with those of a nonlinear global SVM (GSVM) model that was calibrated based on the ten-fold cross-validation method. This study then evaluated an integrated SVM model (LGSVM) obtained based on a weighted combination of the local and global urban development probabilities. A comparison of the probability maps showed a higher accuracy for the LGSVM than for either the LSVM or GSVM model. To assess the performance of the LSVM, GSVM and LGSVM models in the simulation of urban growth, probability maps were employed as the transition rules for urban cellular automata. The results show that a trade-off between local and global SVM models can enhance the performance of urban growth modelling.
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Šimenc, Laura, Urška Kuhar, Urška Jamnikar-Ciglenečki, and Ivan Toplak. "First Complete Genome of Lake Sinai Virus Lineage 3 and Genetic Diversity of Lake Sinai Virus Strains From Honey Bees and Bumble Bees." Journal of Economic Entomology 113, no. 3 (March 24, 2020): 1055–61. http://dx.doi.org/10.1093/jee/toaa049.

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Abstract The complete genome of Lake Sinai virus 3 (LSV3) was sequenced by the Ion Torrent next-generation sequencing (NGS) technology from an archive sample of honey bees collected in 2010. This strain M92/2010 is the first complete genome sequence of LSV lineage 3. From October 2016 to December 2017, 56 honey bee samples from 32 different locations and 41 bumble bee samples from five different locations were collected. These samples were tested using a specific reverse transcriptase-polymerase chain reaction (RT-PCR) method; 75.92% of honey bee samples and 17.07% of bumble bee samples were LSV-positive with the RT-PCR method. Phylogenetic comparison of 557-base pair-long RNA-dependent RNA polymerase (RdRp) genome region of selected 23 positive samples of honey bees and three positive bumble bee samples identified three different LSV lineages: LSV1, LSV2, and LSV3. The LSV3 lineage was confirmed for the first time in Slovenia in 2010, and the same strain was later detected in several locations within the country. The LSV strains detected in bumble bees are from 98.6 to 99.4% identical to LSV strains detected among honey bees in the same territory.
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KHASNOBISH, ANWESHA, ARINDAM JATI, GARIMA SINGH, AMIT KONAR, and D. N. TIBAREWALA. "OBJECT-SHAPE RECOGNITION BY TACTILE IMAGE ANALYSIS USING SUPPORT VECTOR MACHINE." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 04 (June 2014): 1450011. http://dx.doi.org/10.1142/s0218001414500116.

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The sense of touch is important to human to understand shape, texture, and hardness of the objects. An object under grip, i.e. object exploration by enclosure, provides a unique pressure distribution on the different regions of palm depending on its shape. This paper utilizes the above experience for recognition of object shapes by tactile image analysis. The high pressure regions (HPRs) are segmented and analyzed for object shape recognition rather than analyzing the entire image. Tactile images are acquired by capacitive tactile sensor while grasping a particular object. Geometrical features are extracted from the chain codes obtained by polygon approximation of the contours of the segmented HPRs. Two-level classification scheme using linear support vector machine (LSVM) is employed to classify the input feature vector in respective object shape classes with an average classification accuracy of 93.46% and computational time of 1.19 s for 12 different object shape classes. Our proposed two-level LSVM reduces the misclassification rates, thus efficiently recognizes various object shapes from the tactile images.
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Su, Tingting, Shaomin Mu, Aiju Shi, Zhihao Cao, and Mengping Dong. "A CNN-LSVM MODEL FOR IMBALANCED IMAGES IDENTIFICATION OF WHEAT LEAF." Neural Network World 29, no. 5 (2019): 345–61. http://dx.doi.org/10.14311/nnw.2019.29.021.

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Pan, Fei, Baoying Wang, Xin Hu, and William Perrizo. "Comprehensive vertical sample-based KNN/LSVM classification for gene expression analysis." Journal of Biomedical Informatics 37, no. 4 (August 2004): 240–48. http://dx.doi.org/10.1016/j.jbi.2004.07.003.

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Manciu, Marian, Mario Cardenas, Kevin E. Bennet, Avudaiappan Maran, Michael J. Yaszemski, Theresa A. Maldonado, Diana Magiricu, and Felicia S. Manciu. "Assessment of Renal Osteodystrophy via Computational Analysis of Label-free Raman Detection of Multiple Biomarkers." Diagnostics 10, no. 2 (January 31, 2020): 79. http://dx.doi.org/10.3390/diagnostics10020079.

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Accurate clinical evaluation of renal osteodystrophy (ROD) is currently accomplished using invasive in vivo transiliac bone biopsy, followed by in vitro histomorphometry. In this study, we demonstrate that an alternative method for ROD assessment is through a fast, label-free Raman recording of multiple biomarkers combined with computational analysis for predicting the minimally required number of spectra for sample classification at defined accuracies. Four clinically relevant biomarkers: the mineral-to-matrix ratio, the carbonate-to-matrix ratio, phenylalanine, and calcium contents were experimentally determined and simultaneously considered as input to a linear discriminant analysis (LDA). Additionally, sample evaluation was performed with a linear support vector machine (LSVM) algorithm, with a 300 variable input. The computed probabilities based on a single spectrum were only marginally different (~80% from LDA and ~87% from LSVM), both providing an unacceptable classification power for a correct sample assignment. However, the Type I and Type II assignment errors confirm that a relatively small number of independent spectra (7 spectra for Type I and 5 spectra for Type II) is necessary for a p < 0.05 error probability. This low number of spectra supports the practicality of future in vivo Raman translation for a fast and accurate ROD detection in clinical settings.
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Wei, Yanlin, Xiaofeng Li, Xin Pan, and Lei Li. "Nondestructive Classification of Soybean Seed Varieties by Hyperspectral Imaging and Ensemble Machine Learning Algorithms." Sensors 20, no. 23 (December 7, 2020): 6980. http://dx.doi.org/10.3390/s20236980.

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During the processing and planting of soybeans, it is greatly significant that a reliable, rapid, and accurate technique is used to detect soybean varieties. Traditional chemical analysis methods of soybean variety sampling (e.g., mass spectrometry and high-performance liquid chromatography) are destructive and time-consuming. In this paper, a robust and accurate method for nondestructive soybean classification is developed through hyperspectral imaging and ensemble machine learning algorithms. Image acquisition, preprocessing, and feature selection are used to obtain different types of soybean hyperspectral features. Based on these features, one of ensemble classifiers-random subspace linear discriminant (RSLD) algorithm is used to classify soybean seeds. Compared with the linear discrimination (LD) and linear support vector machine (LSVM) methods, the results show that the RSLD algorithm in this paper is more stable and reliable. In classifying soybeans in 10, 15, 20, and 25 categories, the RSLD method achieves the highest classification accuracy. When 155 features are used to classify 15 types of soybeans, the classification accuracy of the RSLD method reaches 99.2%, while the classification accuracies of the LD and LSVM methods are only 98.6% and 69.7%, respectively. Therefore, the ensemble classification algorithm RSLD can maintain high classification accuracy when different types and different classification features are used.
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Sun, Guodong, Yuan Gao, Kai Lin, and Ye Hu. "Fine-Grained Fault Diagnosis Method of Rolling Bearing Combining Multisynchrosqueezing Transform and Sparse Feature Coding Based on Dictionary Learning." Shock and Vibration 2019 (November 20, 2019): 1–13. http://dx.doi.org/10.1155/2019/1531079.

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To accurately diagnose fine-grained fault of rolling bearing, this paper proposed a new fault diagnosis method combining multisynchrosqueezing transform (MSST) and sparse feature coding based on dictionary learning (SFC-DL). Firstly, the high-resolution time-frequency images of raw vibration signals, including different kinds of fine-grained faults of rolling bearing, were constructed by MSST. Then, the basis dictionary was trained through nonnegative matrix factorization with sparseness constraints (NMFSC), and the trained basis dictionary was employed to extract features from time-frequency matrixes by using nonnegative linear equations. Finally, a linear support vector machine (LSVM) was trained with features of training samples, and the trained LSVM was employed to diagnosis the fault classification of test samples. Compared with state-of-the-art fault diagnosis methods, the proposed method, which was tested on the bearing dataset from Case Western Reserve University (CWRU), achieved the fine-grained classification of 10 mixed fault states. Meanwhile, the proposed method was applied on the dataset from the Machinery Failure Prevention Technology (MFPT) Society and realized the classification of 3 fault states under different working conditions. These results indicate that the proposed method has great robustness and could better meet the needs of practical engineering.
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Cornman, Robert S. "Relative abundance and molecular evolution of Lake Sinai Virus (Sinaivirus) clades." PeerJ 7 (March 21, 2019): e6305. http://dx.doi.org/10.7717/peerj.6305.

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Lake Sinai Viruses (Sinaivirus) are commonly detected in honey bees (Apis mellifera) but no disease phenotypes or fitness consequences have yet been demonstrated. This viral group is genetically diverse, lacks obvious geographic structure, and multiple lineages can co-infect individual bees. While phylogenetic analyses have been performed, the molecular evolution of LSV has not been studied extensively. Here, I use LSV isolates from GenBank as well as contigs assembled from honey bee Sequence Read Archive (SRA) accessions to better understand the evolutionary history of these viruses. For each ORF, substitution rate variation, codon usage, and tests of positive selection were evaluated. Outlier regions of high or low diversity were sought with sliding window analysis and the role of recombination in creating LSV diversity was explored. Phylogenetic analysis consistently identified two large clusters of sequences that correspond to the current LSV1 and LSV2 nomenclature, however lineages sister to LSV1 were the most frequently detected in honey bee SRA accessions. Different expression levels among ORFs suggested the occurrence of subgenomic transcripts. ORF1 and RNA-dependent RNA polymerase had higher evolutionary rates than the capsid and ORF4. A hypervariable region of the ORF1 protein-coding sequence was identified that had reduced selective constraint, but a site-based model of positive selection was not significantly more likely than a neutral model for any ORF. The only significant recombination signals detected between LSV1 and LSV2 initiated within this hypervariable region, but assumptions of the test (single-frame coding and independence of substitution rate by site) were violated. LSV codon usage differed strikingly from that of honey bees and other common honey-bee viruses, suggesting LSV is not strongly co-evolved with that host. LSV codon usage was significantly correlated with that of Varroa destructor, however, despite the relatively weak codon bias exhibited by the latter. While codon usage between the LSV1 and LSV2 clusters was similar for three ORFs, ORF4 codon usage was uncorrelated between these clades, implying rapid divergence of codon use for this ORF only. Phylogenetic placement and relative abundance of LSV isolates reconstructed from SRA accessions suggest that detection biases may be over-representing LSV1 and LSV2 in public databases relative to their sister lineages.
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Dissertations / Theses on the topic "LSVM"

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Zaremba, Wojciech. "Modeling the variability of EEG/MEG data through statistical machine learning." Habilitation à diriger des recherches, Ecole Polytechnique X, 2012. http://tel.archives-ouvertes.fr/tel-00803958.

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Brain neural activity generates electrical discharges, which manifest as electrical and magnetic potentials around the scalp. Those potentials can be registered with magnetoencephalography (MEG) and electroencephalography (EEG) devices. Data acquired by M/EEG is extremely difficult to work with due to the inherent complexity of underlying brain processes and low signal-to-noise ratio (SNR). Machine learning techniques have to be employed in order to reveal the underlying structure of the signal and to understand the brain state. This thesis explores a diverse range of machine learning techniques which model the structure of M/EEG data in order to decode the mental state. It focuses on measuring a subject's variability and on modeling intrasubject variability. We propose to measure subject variability with a spectral clustering setup. Further, we extend this approach to a unified classification framework based on Laplacian regularized support vector machine (SVM). We solve the issue of intrasubject variability by employing a model with latent variables (based on a latent SVM). Latent variables describe transformations that map samples into a comparable state. We focus mainly on intrasubject experiments to model temporal misalignment.
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Edholm, Gustav, and Xuechen Zuo. "A comparison between aconventional LSTM network and agrid LSTM network applied onspeech recognition." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230173.

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In this paper, a comparision between the conventional LSTM network and the one-dimensionalgrid LSTM network applied on single word speech recognition is conducted. The performanceof the networks are measured in terms of accuracy and training time. The conventional LSTMmodel is the current state of the art method to model speech recognition. However, thegrid LSTM architecture has proven to be successful in solving other emperical tasks such astranslation and handwriting recognition. When implementing the two networks in the sametraining framework with the same training data of single word audio files, the conventionalLSTM network yielded an accuracy rate of 64.8 % while the grid LSTM network yielded anaccuracy rate of 65.2 %. Statistically, there was no difference in the accuracy rate betweenthe models. In addition, the conventional LSTM network took 2 % longer to train. However,this difference in training time is considered to be of little significance when tralnslating it toabsolute time. Thus, it can be concluded that the one-dimensional grid LSTM model performsjust as well as the conventional one.
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Fu, Reid J. "CCG Realization with LSTM Hypertagging." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1534236955413883.

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Nordin, Stensö Isak. "Predicting Tropical Thunderstorm Trajectories Using LSTM." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231613.

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Thunderstorms are both dangerous as well as important rain-bearing structures for large parts of the world. The prediction of thunderstorm trajectories is however difficult, especially in tropical regions. This is largely due to their smaller size and shorter lifespan. To overcome this issue, this thesis investigates how well a neural network composed of long short-term memory (LSTM) units can predict the trajectories of thunderstorms, based on several years of lightning strike data. The data is first clustered, and important features are extracted from it. These are used to predict the mean position of the thunderstorms using an LSTM network. A random search is then carried out to identify optimal parameters for the LSTM model. It is shown that the trajectories predicted by the LSTM are much closer to the true trajectories than what a linear model predicts. This is especially true for predictions of more than 1 hour. Scores commonly used to measure forecast accuracy are applied to compare the LSTM and linear model. It is found that the LSTM significantly improves forecast accuracy compared to the linear model.
Åskväder är både farliga och livsviktiga bärare av vatten för stora delar av världen. Det är dock svårt att förutsäga åskcellernas banor, främst i tropiska områden. Detta beror till större delen på deras mindre storlek och kortare livslängd. Detta examensarbete undersöker hur väl ett neuralt nätverk, bestående av long short-term memory-lager (LSTM) kan förutsäga åskväders banor baserat på flera års blixtnedlslagsdata. Först klustras datan, och viktiga karaktärsdrag hämtas ut från den. Dessa används för att förutspå åskvädrens genomsnittliga position med hjälp av ett LSTMnätverk. En slumpmässig sökning genomförs sedan för att identifiera optimala parametrar för LSTM-modellen. Det fastslås att de banor som förutspås av LSTM-modellen är mycket närmare de sanna banorna, än de som förutspås av en linjär modell. Detta gäller i synnerhet för förutsägelser mer än 1 timme framåt. Värden som är vanliga för att bedöma prognosers träffsäkerhet beräknas för att jämföra LSTM-modellen och den linjära. Det visas att LSTM-modellen klart förbättrar förutsägelsernas träffsäkerhet jämfört med den linjära modellen.
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Rogers, Joseph. "Effects of an LSTM Composite Prefetcher." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-396842.

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Recent work in computer architecture and machine learning has seen various groups begin exploring the viability of using neural networks to augment conventional processor designs. Of particular interest is using the predictive capabilities of techniques in natural language processing to assist traditional CPU memory prefetching methods. This work demonstrates one of these proposed techniques, and examines some of the challenges associated with producing satisfactory and consistently reproducible results. Special attention is given to data acquisition and preprocessing as different methods. This is important since the handling training data can enormously influence on the final prediction accuracy of the network. Finally, a number of changes to improve these methods are suggested. These include ways to raise accuracy, reduce network overhead, and to improve the consistency of results. This work shows that augmenting an LSTM prefetcher with a simple stream prefetcher leads to moderate improvements in prediction accuracy. This could be a way to start reducing the size of neural networks so they are usable in real hardware.
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Buffat, Marcel. "Die LSVA und die Standortattraktivität peripherer Regionen." St. Gallen, 2008. http://www.biblio.unisg.ch/org/biblio/edoc.nsf/wwwDisplayIdentifier/04104089001/$FILE/04104089001.pdf.

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Nilson, Erik, and Arvid Renström. "LSTM-nätverk för generellt Atari 2600 spelande." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17174.

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I detta arbete jämfördes ett LSTM-nätverk med ett feedforward-nätverk för generellt Atari 2600 spelande. Prestandan definierades som poängen agenten får för ett visst spel. Hypotesen var att LSTM skulle prestera minst lika bra som feedforward och förhoppningsvis mycket bättre. För att svara på frågeställningen skapades två olika agenter, en med ett LSTM-nätverk och en med ett feedforward-nätverk. Experimenten utfördes på Stella emulatorn med hjälp av ramverket the Arcade Learning Environment (ALE). Hänsyn togs till Machado råd om inställningar för användning av ALE och hur agenter borde tränas och evalueras samtidigt. Agenterna utvecklades med hjälp av en genetisk algoritm. Resultaten visade att LSTM var minst lika bra som feedforward men båda metoderna blev slagna av Machados metoder. Toppoängen i varje spel jämfördes med Granfelts arbete som har varit en utgångspunkt för detta arbete.
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Paschou, Michail. "ASIC implementation of LSTM neural network algorithm." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254290.

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LSTM neural networks have been used for speech recognition, image recognition and other artificial intelligence applications for many years. Most applications perform the LSTM algorithm and the required calculations on cloud computers. Off-line solutions include the use of FPGAs and GPUs but the most promising solutions include ASIC accelerators designed for this purpose only. This report presents an ASIC design capable of performing the multiple iterations of the LSTM algorithm on a unidirectional and without peepholes neural network architecture. The proposed design provides arithmetic level parallelism options as blocks are instantiated based on parameters. The internal structure of the design implements pipelined, parallel or serial solutions depending on which is optimal in every case. The implications concerning these decisions are discussed in detail in the report. The design process is described in detail and the evaluation of the design is also presented to measure accuracy and error of the design output.This thesis work resulted in a complete synthesizable ASIC design implementing an LSTM layer, a Fully Connected layer and a Softmax layer which can perform classification of data based on trained weight matrices and bias vectors. The design primarily uses 16-bit fixed point format with 5 integer and 11 fractional bits but increased precision representations are used in some blocks to reduce error output. Additionally, a verification environment has also been designed and is capable of performing simulations, evaluating the design output by comparing it with results produced from performing the same operations with 64-bit floating point precision on a SystemVerilog testbench and measuring the encountered error. The results concerning the accuracy and the design output error margin are presented in this thesis report. The design went through Logic and Physical synthesis and successfully resulted in a functional netlist for every tested configuration. Timing, area and power measurements on the generated netlists of various configurations of the design show consistency and are reported in this report.
LSTM neurala nätverk har använts för taligenkänning, bildigenkänning och andra artificiella intelligensapplikationer i många år. De flesta applikationer utför LSTM-algoritmen och de nödvändiga beräkningarna i digitala moln. Offline lösningar inkluderar användningen av FPGA och GPU men de mest lovande lösningarna inkluderar ASIC-acceleratorer utformade för endast dettaändamål. Denna rapport presenterar en ASIC-design som kan utföra multipla iterationer av LSTM-algoritmen på en enkelriktad neural nätverksarkitetur utan peepholes. Den föreslagna designed ger aritmetrisk nivå-parallellismalternativ som block som är instansierat baserat på parametrar. Designens inre konstruktion implementerar pipelinerade, parallella, eller seriella lösningar beroende på vilket anternativ som är optimalt till alla fall. Konsekvenserna för dessa beslut diskuteras i detalj i rapporten. Designprocessen beskrivs i detalj och utvärderingen av designen presenteras också för att mäta noggrannheten och felmarginal i designutgången. Resultatet av arbetet från denna rapport är en fullständig syntetiserbar ASIC design som har implementerat ett LSTM-lager, ett fullständigt anslutet lager och ett Softmax-lager som kan utföra klassificering av data baserat på tränade viktmatriser och biasvektorer. Designen använder huvudsakligen 16bitars fast flytpunktsformat med 5 heltal och 11 fraktions bitar men ökade precisionsrepresentationer används i vissa block för att minska felmarginal. Till detta har även en verifieringsmiljö utformats som kan utföra simuleringar, utvärdera designresultatet genom att jämföra det med resultatet som produceras från att utföra samma operationer med 64-bitars flytpunktsprecision på en SystemVerilog testbänk och mäta uppstådda felmarginal. Resultaten avseende noggrannheten och designutgångens felmarginal presenteras i denna rapport.Designen gick genom Logisk och Fysisk syntes och framgångsrikt resulterade i en funktionell nätlista för varje testad konfiguration. Timing, area och effektmätningar på den genererade nätlistorna av olika konfigurationer av designen visar konsistens och rapporteras i denna rapport.
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Valluru, Aravind-Deshikh. "Realization of LSTM Based Cognitive Radio Network." Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1538697/.

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This thesis presents the realization of an intelligent cognitive radio network that uses long short term memory (LSTM) neural network for sensing and predicting the spectrum activity at each instant of time. The simulation is done using Python and GNU Radio. The implementation is done using GNU Radio and Universal Software Radio Peripherals (USRP). Simulation results show that the confidence factor of opportunistic users not causing interference to licensed users of the spectrum is 98.75%. The implementation results demonstrate high reliability of the LSTM based cognitive radio network.
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Schelhaas, Wietze. "Predicting network performancein IoT environments using LSTM." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-454062.

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There are still many problems that need to be solved with Internet of Things (IoT) technology, one of them being performance assurance. To ensure a certain quality of service in an IoT environment, the network has to be monitored and actively measured. However, Due to the limited computational recourses Internet of things nodes have, active measurement is difficult to achieve without also inducing energy and network overhead. A potential solution to this problem is to apply a machine-learning algorithm to predict network performance metrics such as round- trip time or packet loss. By substituting active performance measurements with a machine-learning algorithm, you reduce the overhead created by active performance measurements Previous research has revolved around applying traditional machine learning algorithms to wireless sensor network features such as packet statistics and topological information of the network to predict round-trip time. The purpose of this thesis is to use a  more advanced deep learning algorithm namely Long short-term memory (LSTM) to try and exploit time dependencies in the data Three different datasets containing network statistics are used in three different experiments. In every experiment, LSTM models with different configurations are created, and their predictioncapabilities are compared to traditional neural networks with equivalent configurations. In all experiments, both the LSTM model and its corresponding equivalent neural network model produced similar results, meaning that a time dependency in the data could not be proven.
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Books on the topic "LSVM"

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Ecip, S. Sinansari. LSM sariawan? Jakarta: Pustaka Firdaus, 1995.

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Van, Sona. Es dzayn em lsum. Erevan: Graber, 2006.

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Long, Tùng. Muot lsan lsam lzo. TP. HCM [i.e. Thành phro Hso Chí Minh]: NXB Văn nghue, 2008.

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Trung, Sĩ. Muot lsan lsam lzo: Truyuen. Paris: Nam Á, 1986.

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Lang, Trọng. Hà Nuoi lsam than: Phóng svu. Los Alamitos, CA: Xuân Thu, 1990.

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Jakarta?, Indonesia) Lokakarya Program Pemberdayaan Masyarakat Lewat Ketahanan Pangan (2000. Pemberdayaan masyarakat melalui ketahanan pangan: Kajian empiris LSM-LSM mitra Yayasan Indonesia Sejahtera. Jakarta: Yayasan Indonesia Sejahtera, 2001.

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Goodyear, C. P. LSIM, a length-based fish population simulation model. Miami, Fla: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Southeast Fisheries Center, Miami Laboratory, 1989.

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Goodyear, C. P. LSIM, a length-based fish population simulation model. Miami, Fla: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Southeast Fisheries Center, Miami Laboratory, 1989.

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Bastian, Indra. Akuntansi untuk LSM dan partai politik. Ciracas, Jakarta: Penerbit Erlangga, 2007.

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Firdous. Respons LSM terhadap perdagangan anak perempuan. Edited by Putranti Basilica Dyah, Casmiwati Dewi, Universitas Gadjah Mada. Pusat Studi Kependudukan dan Kebijakan., and Ford Foundation. Yogyakarta: Kerja sama Ford Foundation dengan Pusat Studi Kependudukan dan Kebijakan, Universitas Gadjah Mada, 2004.

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Book chapters on the topic "LSVM"

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Mohammadi, Alidad, Nigel M. Sammes, Jakub Pusz, and Alevtina L. Smirnova. "Anode Supported LSCM-LSGM-LSM Solid Oxide Fuel Cell." In Advances in Solid Oxide Fuel Cells II: Ceramic Engineering and Science Proceedings, Volume 27, Issue 4, 27–34. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2008. http://dx.doi.org/10.1002/9780470291337.ch3.

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Korstanje, Joos. "LSTM RNNs." In Advanced Forecasting with Python, 243–51. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7150-6_18.

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Eelbode, Tom, Pieter Sinonquel, Raf Bisschops, and Frederik Maes. "Convolutional LSTM." In Computer-Aided Analysis of Gastrointestinal Videos, 121–26. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64340-9_14.

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Wang, Ximin, Luyi Huang, Junlan Zhu, Wenbo He, Zhaopeng Qin, and Ming Yuan. "LSTM-Exploit: Intelligent Penetration Based on LSTM Tool." In Advances in Artificial Intelligence and Security, 84–93. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78615-1_8.

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Adam, Kazybek, Kamilya Smagulova, and Alex Pappachen James. "Memristive LSTM Architectures." In Modeling and Optimization in Science and Technologies, 155–67. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14524-8_12.

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Manaswi, Navin Kumar. "RNN and LSTM." In Deep Learning with Applications Using Python, 115–26. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3516-4_9.

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Bakalos, Nikolaos, Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Kassiani Papasotiriou, and Matthaios Bimpas. "Fusing RGB and Thermal Imagery with Channel State Information for Abnormal Activity Detection Using Multimodal Bidirectional LSTM." In Cyber-Physical Security for Critical Infrastructures Protection, 77–86. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69781-5_6.

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AbstractIn this paper, we present a multimodal deep model for detection of abnormal activity, based on bidirectional Long Short-Term Memory neural networks (LSTM). The proposed model exploits three different input modalities: RGB imagery, thermographic imagery and Channel State Information from Wi-Fi signal reflectance to estimate human intrusion and suspicious activity. The fused multimodal information is used as input in a Bidirectional LSTM, which has the benefit of being able to capture temporal interdependencies in both past and future time instances, a significant aspect in the discussed unusual activity detection scenario. We also present a Bayesian optimization framework that fine-tunes the Bidirectional LSTM parameters in an optimal manner. The proposed framework is evaluated on real-world data from a critical water infrastructure protection and monitoring scenario and the results indicate a superior performance compared to other unimodal and multimodal approaches and classification models.
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Huynh, Manh, and Gita Alaghband. "Trajectory Prediction by Coupling Scene-LSTM with Human Movement LSTM." In Advances in Visual Computing, 244–59. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33720-9_19.

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Grósz, Tamás, and Mikko Kurimo. "LSTM-XL: Attention Enhanced Long-Term Memory for LSTM Cells." In Text, Speech, and Dialogue, 382–93. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83527-9_32.

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Duan, Tiehang, and Sargur N. Srihari. "Layerwise Interweaving Convolutional LSTM." In Advances in Artificial Intelligence, 272–77. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57351-9_31.

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Conference papers on the topic "LSVM"

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Chen, Jie ping. "A handwritten signature recognition system based on LSVM." In 2015 International Conference on Computational Science and Engineering. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/iccse-15.2015.89.

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Prabha, G., and S. Natarajamani. "Adaptive Beamforming using LSVM Algorithm for Radar Applications." In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV). IEEE, 2021. http://dx.doi.org/10.1109/icicv50876.2021.9388387.

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Kalshaonkar, Reema, and Sonia Kuwelkar. "Design of an accurate pedestrian detection system using modified HOG and LSVM." In 2017 International Conference on Computing, Communication and Automation (ICCCA). IEEE, 2017. http://dx.doi.org/10.1109/ccaa.2017.8229945.

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Wan, Yi, Chengwen Wu, and Yangu Zhang. "Notice of Retraction: Electro-Hydraulic Proportional Self-Adaptive Controller Based on LSVM Intelligent Algorithm." In 2008 Fourth International Conference on Natural Computation (ICNC). IEEE, 2008. http://dx.doi.org/10.1109/icnc.2008.312.

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Do, Thanh-Nghi, and Francois Poulet. "Classifying Very High-Dimensional and Large-Scale Multi-class Image Datasets with Latent-lSVM." In 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld). IEEE, 2016. http://dx.doi.org/10.1109/uic-atc-scalcom-cbdcom-iop-smartworld.2016.0116.

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Yi, Jae Yeon, and Gyeong Man Choi. "PHASE CHARACTERIZATION AND ELECTRICAL PROPERTIES OF LSM-LSGM SYSTEM." In Proceedings of the 7th Asian Conference. WORLD SCIENTIFIC, 2000. http://dx.doi.org/10.1142/9789812791979_0083.

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Zhang, Liming, Bo Wang, Biwu Fang, Hengrui Ma, Zheng Yang, and Yeyan Xu. "Two-Stage Short-Term Wind Speed Prediction Based on LSTM-LSSVM-CFA." In 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2018. http://dx.doi.org/10.1109/ei2.2018.8582618.

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Zhou, Yuxin, Jing Shi, Hongkun Chen, and Tong Ding. "Interval Prediction of Photovoltaic Output Based on WOA-LSTM-LSSVM Combined Model." In 2021 6th Asia Conference on Power and Electrical Engineering (ACPEE). IEEE, 2021. http://dx.doi.org/10.1109/acpee51499.2021.9436884.

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Jena, Hrudananda, and B. Rambabu. "Effect of Sonochemical, Regenerative Sol Gel and Microwave Assisted Synthesis Techniques on the Formation of Dense Electrolytes and Porus Electrodes for All Perovskite IT-SOFCs." In ASME 2006 4th International Conference on Fuel Cell Science, Engineering and Technology. ASMEDC, 2006. http://dx.doi.org/10.1115/fuelcell2006-97262.

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The influence of preparation techniques on the microstructure, grain-size and consequently on the electrical transport properties of the ABO3 structured materials used as electrode and electrolytes in all perovskite IT-SOFC were investigated. Nano-crystalline powders of La1-xMxGa1-yNyO3±δ (M = Sr,; x = −0.10 to 0.15; N = Mg; y = −0.10 to 0.15) (LSGM) as electrolyte, porous La0.8Sr0.2Co0.8Fe0.2O3±δ (LSCF) or LaNi1-xFexO3±δ (x = 0–0.5) (LNF) as cathode, La0.8Sr0.2Cr0.7Mn0.3O3±δ (LSCM) as anode and LaCrO3 or substituted LaCrO 3 as interconnect were synthesized by various wet chemical methods. The wet chemical methods like metal-carboxylate gel decomposition, hydroxide co-precipitation, sonochemical and regenerative sol-gel process followed by microwave sintering of the powders have been used. Microwave sintering parameters were optimized by varying sintering time, and temperature to achieve higher density of LSGM pellets. The phase pure systems were obtained at sintering duration of 30 min at 1200 °C. The XRD, HR-TEM, and SEM measurements revealed the average grain size of these perovskites was ∼ 22 nm range. The electrical conductivities of the compositions were measured by ac (5Hz–13MHz) and dc techniques. The conductivity of the sintered pellets was found to be ∼0.01–0.21 S/cm at 550–1000°C range for electrolyte and 1.5–100 S/cm at 25–1000°C for electrodes respectively. The effect of sonochemical, and regenerative sol-gel methods in processing large quantities of nano-crystalline perovskites with multi-element substitutions at A- and B-sites to achieve physico-chemical compatibility for fabricating zero emission all perovskite IT-SOFCs are reported in this paper.
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Xing, Bowen, Lejian Liao, Dandan Song, Jingang Wang, Fuzheng Zhang, Zhongyuan Wang, and Heyan Huang. "Earlier Attention? Aspect-Aware LSTM for Aspect-Based Sentiment Analysis." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/738.

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Aspect-based sentiment analysis (ABSA) aims to predict fine-grained sentiments of comments with respect to given aspect terms or categories. In previous ABSA methods, the importance of aspect has been realized and verified. Most existing LSTM-based models take aspect into account via the attention mechanism, where the attention weights are calculated after the context is modeled in the form of contextual vectors. However, aspect-related information may be already discarded and aspect-irrelevant information may be retained in classic LSTM cells in the context modeling process, which can be improved to generate more effective context representations. This paper proposes a novel variant of LSTM, termed as aspect-aware LSTM (AA-LSTM), which incorporates aspect information into LSTM cells in the context modeling stage before the attention mechanism. Therefore, our AA-LSTM can dynamically produce aspect-aware contextual representations. We experiment with several representative LSTM-based models by replacing the classic LSTM cells with the AA-LSTM cells. Experimental results on SemEval-2014 Datasets demonstrate the effectiveness of AA-LSTM.
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Reports on the topic "LSVM"

1

Rej, D. J., W. N. Hugrass, G. A. Barnes, and R. E. Siemon. FRC formation experiments with tearing reconnection on the FRX-C/LSM device. Office of Scientific and Technical Information (OSTI), September 1986. http://dx.doi.org/10.2172/7153110.

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De Guire, Mark. Operating Stresses and their Effects on Degradation of LSM-Based SOFC Cathodes. Office of Scientific and Technical Information (OSTI), June 2021. http://dx.doi.org/10.2172/1804272.

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Ankel, Victoria, Stella Pantopoulou, Matthew Weathered, Darius Lisowski, Anthonie Cilliers, and Alexander Heifetz. One-Step Ahead Prediction of Thermal Mixing Tee Sensors with Long Short Term Memory (LSTM) Neural Networks. Office of Scientific and Technical Information (OSTI), December 2020. http://dx.doi.org/10.2172/1760289.

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Rej, D. J. Electron temperature measurements of field-reversed configuration plasmas on the FRX-C/LSM experiment. Office of Scientific and Technical Information (OSTI), September 1989. http://dx.doi.org/10.2172/5866713.

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De Guire, Mark. Long Term Degradation of LSM Based SOFC Cathodes: Use of a Proven Accelerated Test Regimen. Office of Scientific and Technical Information (OSTI), January 2020. http://dx.doi.org/10.2172/1592169.

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Qin, Changyong, and Kevin Huang. Theoretical Design and Experimental Evaluation of Molten Carbonate Modified LSM Cathode for Low Temperature Solid Oxide Fuel Cells. Fort Belvoir, VA: Defense Technical Information Center, January 2015. http://dx.doi.org/10.21236/ada621605.

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Qin, Changyong, and Kevin Huang. Theoretical Design and Experimental Evaluation of Molten Carbonate Modified LSM Cathode for Low Temperature Solid Oxide Fuel Cells. Fort Belvoir, VA: Defense Technical Information Center, January 2012. http://dx.doi.org/10.21236/ada581764.

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Hepworth, Nick. Reading Pack: Tackling the Global Water Crisis: The Role of Water Footprints and Water Stewardship. Institute of Development Studies (IDS), August 2021. http://dx.doi.org/10.19088/k4d.2021.109.

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The K4D professional development Reading Packs provide thought-provoking introductions by international experts and highlight the emerging issues and debates within them. They aim to help inform policies that are more resilient to the future. K4D services are provided by a consortium of leading organisations working in international development, led by the Institute of Development Studies (IDS), with the Education Development Trust, Itad, University of Leeds Nuffield Centre for International Health and Development, Liverpool School of Tropical Medicine (LSTM), University of Birmingham International Development Department (IDD) and the University of Manchester Humanitarian and Conflict Response Institute (HCRI). For any enquiries, please contact helpdesk@k4d.info
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CAPACITY EVALUATION OF EIGHT BOLT EXTENDED ENDPLATE MOMENT CONNECTIONS SUBJECTED TO COLUMN REMOVAL SCENARIO. The Hong Kong Institute of Steel Construction, September 2021. http://dx.doi.org/10.18057/ijasc.2021.17.3.6.

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The extended stiffened endplate (8ES) connection is broadly used in the seismic load-resisting parts of steel structures. This connection is prequalified based on the AISC 358 standard, especially for seismic regions. To study this connection’s behaviors, in the event of accidental loss of a column, the finite element model results were verified against the available experimental data. A parametric study using the finite element method was then carried out to investigate these numerical models’ maximum capacity and effective parameters' effect on their maximum capacity in a column loss scenario. This parametric analysis demonstrated that these connections fail at the large displacement due to the catenary action mode at the rib stiffener's vicinity. The carrying capacity, PEEQ, Von-Mises stress, middle column force-displacement, critical bolt axial load, and the beam axial load curves were discussed. Finally, using the Least Square Method (LSM), a formula is presented to determine the displacement at the maximum capacity of these connections. This formula can be used in this study's presented method to determine the maximum load capacity of the 8ES connections in a column loss scenario.
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