Journal articles: 'DeepSORT'

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Zhang, Limin, Jie Jiang, Wei Fang, and Kai Liu. "Real TimeDetection and Tracking Method of Pilot’sHeadPositionBased on MTCNN-DeepSORT." Journal of Physics: Conference Series 1682 (November 2020): 012025. http://dx.doi.org/10.1088/1742-6596/1682/1/012025.

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Wang, An, Xiaohong Cao, Lei Lu, Xinjing Zhou, and Xuecheng Sun. "Design of Efficient Human Head Statistics System in the Large-Angle Overlooking Scene." Electronics 10, no. 15 (July 31, 2021): 1851. http://dx.doi.org/10.3390/electronics10151851.

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Human head statistics is widely used in the construction of smart cities and has great market value. In order to solve the problem of missing pedestrian features and poor statistics results in a large-angle overlooking scene, in this paper we propose a human head statistics system that consists of head detection, head tracking and head counting, where the proposed You-Only-Look-Once-Head (YOLOv5-H) network, improved from YOLOv5, is taken as the head detection benchmark, the DeepSORT algorithm with the Fusion-Hash algorithm for feature extraction (DeepSORT-FH) is proposed to track heads, and heads are counted by the proposed cross-boundary counting algorithm based on scene segmentation. Specifically, Complete-Intersection-over-Union (CIoU) is taken as the loss function of YOLOv5-H to make the predicted boxes more in line with the real boxes. The results demonstrate that the recall rate and mAP@.5 of the proposed YOLOv5-H can reach up to 94.3% and 93.1%, respectively, on the SCUT_HEAD dataset. The statistics system has an extremely low error rate of 3.5% on the TownCentreXVID dataset while maintaining a frame rate of 18FPS, which can meet the needs of human head statistics in monitoring scenarios and has a good application prospect.

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Liu, Chieh-Min, and Jyh-Ching Juang. "Estimation of Lane-Level Traffic Flow Using a Deep Learning Technique." Applied Sciences 11, no. 12 (June 17, 2021): 5619. http://dx.doi.org/10.3390/app11125619.

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This paper proposes a neural network that fuses the data received from a camera system on a gantry to detect moving objects and calculate the relative position and velocity of the vehicles traveling on a freeway. This information is used to estimate the traffic flow. To estimate the traffic flows at both microscopic and macroscopic levels, this paper used YOLO v4 and DeepSORT for vehicle detection and tracking. The number of vehicles passing on the freeway was then calculated by drawing virtual lines and hot zones. The velocity of each vehicle was also recorded. The information can be passed to the traffic control center in order to monitor and control the traffic flows on freeways and analyze freeway conditions.

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Liu, Xin, and Zhanyue Zhang. "A Vision-Based Target Detection, Tracking, and Positioning Algorithm for Unmanned Aerial Vehicle." Wireless Communications and Mobile Computing 2021 (April 10, 2021): 1–12. http://dx.doi.org/10.1155/2021/5565589.

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Unmanned aerial vehicles (UAV) play a pivotal role in the field of security owing to their flexibility, efficiency, and low cost. The realization of vehicle target detection, tracking, and positioning from the perspective of a UAV can effectively improve the efficiency of urban intelligent traffic monitoring. In this work, by fusing the target detection network, YOLO v4, with the detection-based multitarget tracking algorithm, DeepSORT, a method based on deep learning for automatic vehicle detection and tracking in urban environments, has been designed. With the aim of addressing the problem of UAV positioning a vehicle target, the state equation and measurement equation of the system have been constructed, and a particle filter based on interactive multimodel has been employed for realizing the state estimation of the maneuvering target in the nonlinear system. Results of the simulation show that the algorithm proposed in this work can detect and track vehicles automatically in urban environments. In addition, the particle filter algorithm based on an interactive multimodel significantly improves the performance of the UAV in terms of positioning the maneuvering targets, and this has good engineering application value.

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Shin, Minchan, and Nammee Moon. "Indoor Distance Measurement System COPS (COVID-19 Prevention System)." Sustainability 13, no. 9 (April 23, 2021): 4738. http://dx.doi.org/10.3390/su13094738.

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With the rapid spread of coronavirus disease 2019 (COVID-19), measures are needed to detect social distancing and prevent further infection. In this paper, we propose a system that detects social distancing in indoor environments and identifies the movement path and contact objects according to the presence or absence of an infected person. This system detects objects through frames of video data collected from a closed-circuit television using You Only Look Once (v. 4) and assigns and tracks object IDs using DeepSORT, a multiple object tracking algorithm. Next, the coordinates of the detected object are transformed by image warping the area designated by the top angle composition in the original frame. The converted coordinates are matched with the actual map to measure the distance between objects and detect the social distance. If an infected person is present, the object that violates the movement path and social distancing of the infected person is detected using the ID assigned to each object. The proposed system can be used to prevent the rapid spread of infection by detecting social distancing and detecting and tracking objects according to the presence of infected persons.

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Huang, Wei, Xiaoshu Zhou, Mingchao Dong, and Huaiyu Xu. "Multiple objects tracking in the UAV system based on hierarchical deep high-resolution network." Multimedia Tools and Applications 80, no. 9 (January 19, 2021): 13911–29. http://dx.doi.org/10.1007/s11042-020-10427-1.

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AbstractRobust and high-performance visual multi-object tracking is a big challenge in computer vision, especially in a drone scenario. In this paper, an online Multi-Object Tracking (MOT) approach in the UAV system is proposed to handle small target detections and class imbalance challenges, which integrates the merits of deep high-resolution representation network and data association method in a unified framework. Specifically, while applying tracking-by-detection architecture to our tracking framework, a Hierarchical Deep High-resolution network (HDHNet) is proposed, which encourages the model to handle different types and scales of targets, and extract more effective and comprehensive features during online learning. After that, the extracted features are fed into different prediction networks for interesting targets recognition. Besides, an adjustable fusion loss function is proposed by combining focal loss and GIoU loss to solve the problems of class imbalance and hard samples. During the tracking process, these detection results are applied to an improved DeepSORT MOT algorithm in each frame, which is available to make full use of the target appearance features to match one by one on a practical basis. The experimental results on the VisDrone2019 MOT benchmark show that the proposed UAV MOT system achieves the highest accuracy and the best robustness compared with state-of-the-art methods.

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Yoshimori, Atsushi, and Jürgen Bajorath. "Deep SAR matrix: SAR matrix expansion for advanced analog design using deep learning architectures." Future Drug Discovery 2, no. 2 (April 1, 2020): FDD36. http://dx.doi.org/10.4155/fdd-2020-0005.

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Aim: Enhancing the structure–activity relationship matrix (SARM) methodology through integration of deep learning and expansion of chemical space coverage. Background: Analog design is of critical importance for medicinal chemistry. The SARM approach, which combines systematic structural organization of compound series with analog design, is put into scientific context. Methodology: The new DeepSARM concept is introduced. The architecture of SARM-integrated deep generative models is detailed and the workflow for advanced analog design and matrix expansion described. Exemplary application: The DeepSARM approach is applied to design analogs of kinase inhibitors taking kinome-wide chemical space into account. Future perspective: Practical applications of DeepSARM will be a major focal point. Different applications are discussed. New computational features will be added to prioritize virtual candidate compounds.

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Guo, Bin, Ziqi Wang, Pei Wang, Tong Xin, Daqing Zhang, and Zhiwen Yu. "DeepStore: Understanding Customer Behaviors in Unmanned Stores." IT Professional 22, no. 3 (May 1, 2020): 55–63. http://dx.doi.org/10.1109/mitp.2019.2928272.

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Chen, Jin-Bor, Huai-Shuo Yang, Sin-Hua Moi, Li-Yeh Chuang, and Cheng-Hong Yang. "Identification of mortality-risk-related missense variant for renal clear cell carcinoma using deep learning." Therapeutic Advances in Chronic Disease 12 (January 2021): 204062232199262. http://dx.doi.org/10.1177/2040622321992624.

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Introduction: Kidney renal clear cell carcinoma (KIRCC) is a highly heterogeneous and lethal cancer that can arise in patients with renal disease. DeepSurv combines a deep feed-forward neural network with a Cox proportional hazards function and could provide optimized survival results compared with convenient survival analysis. Methods: This study used an improved DeepSurv algorithm to identify the candidate genes to be targeted for treatment on the basis of the overall mortality status of KIRCC subjects. All the somatic mutation missense variants of KIRCC subjects were abstracted from TCGA-KIRC database. Results: The improved DeepSurv model (95.1%) achieved greater balanced accuracy compared with the DeepSurv model (75%), and identified 610 high-risk variants associated with overall mortality. The results of gene differential expression analysis also indicated nine KIRCC mortality-risk-related pathways, namely the tRNA charging pathway, the D-myo-inositol-5-phosphate metabolism pathway, the DNA double-strand break repair by nonhomologous end-joining pathway, the superpathway of inositol phosphate compounds, the 3-phosphoinositide degradation pathway, the production of nitric oxide and reactive oxygen species in macrophages pathway, the synaptic long-term depression pathway, the sperm motility pathway, and the role of JAK2 in hormone-like cytokine signaling pathway. The biological findings in this study indicate the KIRCC mortality-risk-related pathways were more likely to be associated with cancer cell growth, cancer cell differentiation, and immune response inhibition. Conclusion: The results proved that the improved DeepSurv model effectively classified mortality-related high-risk variants and identified the candidate genes. In the context of KIRCC overall mortality, the proposed model effectively recognized mortality-related high-risk variants for KIRCC.

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Yoshimori, Atsushi, Huabin Hu, and Jürgen Bajorath. "Adapting the DeepSARM approach for dual-target ligand design." Journal of Computer-Aided Molecular Design 35, no. 5 (March 13, 2021): 587–600. http://dx.doi.org/10.1007/s10822-021-00379-5.

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AbstractThe structure–activity relationship (SAR) matrix (SARM) methodology and data structure was originally developed to extract structurally related compound series from data sets of any composition, organize these series in matrices reminiscent of R-group tables, and visualize SAR patterns. The SARM approach combines the identification of structural relationships between series of active compounds with analog design, which is facilitated by systematically exploring combinations of core structures and substituents that have not been synthesized. The SARM methodology was extended through the introduction of DeepSARM, which added deep learning and generative modeling to target-based analog design by taking compound information from related targets into account to further increase structural novelty. Herein, we present the foundations of the SARM methodology and discuss how DeepSARM modeling can be adapted for the design of compounds with dual-target activity. Generating dual-target compounds represents an equally attractive and challenging task for polypharmacology-oriented drug discovery. The DeepSARM-based approach is illustrated using a computational proof-of-concept application focusing on the design of candidate inhibitors for two prominent anti-cancer targets.

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Sung, Tegg Taekyong, Jeongsoo Ha, Jeewoo Kim, Alex Yahja, Chae-Bong Sohn, and Bo Ryu. "DeepSoCS: A Neural Scheduler for Heterogeneous System-on-Chip (SoC) Resource Scheduling." Electronics 9, no. 6 (June 4, 2020): 936. http://dx.doi.org/10.3390/electronics9060936.

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In this paper, we present a novel scheduling solution for a class of System-on-Chip (SoC) systems where heterogeneous chip resources (DSP, FPGA, GPU, etc.) must be efficiently scheduled for continuously arriving hierarchical jobs with their tasks represented by a directed acyclic graph. Traditionally, heuristic algorithms have been widely used for many resource scheduling domains, and Heterogeneous Earliest Finish Time (HEFT) has been a dominating state-of-the-art technique across a broad range of heterogeneous resource scheduling domains over many years. Despite their long-standing popularity, HEFT-like algorithms are known to be vulnerable to a small amount of noise added to the environment. Our Deep Reinforcement Learning (DRL)-based SoC Scheduler (DeepSoCS), capable of learning the “best” task ordering under dynamic environment changes, overcomes the brittleness of rule-based schedulers such as HEFT with significantly higher performance across different types of jobs. We describe a DeepSoCS design process using a real-time heterogeneous SoC scheduling emulator, discuss major challenges, and present two novel neural network design features that lead to outperforming HEFT: (i) hierarchical job- and task-graph embedding; and (ii) efficient use of real-time task information in the state space. Furthermore, we introduce effective techniques to address two fundamental challenges present in our environment: delayed consequences and joint actions. Through an extensive simulation study, we show that our DeepSoCS exhibits the significantly higher performance of job execution time than that of HEFT with a higher level of robustness under realistic noise conditions. We conclude with a discussion of the potential improvements for our DeepSoCS neural scheduler.

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Musgrove, Michael, Joseph Harmon, Youssef M. A. Hashash, and Ellen Rathje. "Evaluation of the DEEPSOIL Software on the DesignSafe Cyberinfrastructure." Journal of Geotechnical and Geoenvironmental Engineering 143, no. 9 (September 2017): 02817005. http://dx.doi.org/10.1061/(asce)gt.1943-5606.0001755.

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Choudhury, Deepankar, and Purnanand Savoikar. "Equivalent-linear seismic analyses of MSW landfills using DEEPSOIL." Engineering Geology 107, no. 3-4 (August 2009): 98–108. http://dx.doi.org/10.1016/j.enggeo.2009.05.004.

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Oei, Ronald Wihal, Yingchen Lyu, Lulu Ye, Fangfang Kong, Chengrun Du, Ruiping Zhai, Tingting Xu, et al. "Progression-Free Survival Prediction in Patients with Nasopharyngeal Carcinoma after Intensity-Modulated Radiotherapy: Machine Learning versus Traditional Statistics." Journal of Personalized Medicine 11, no. 8 (August 12, 2021): 787. http://dx.doi.org/10.3390/jpm11080787.

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Background: The Cox proportional hazards (CPH) model is the most commonly used statistical method for nasopharyngeal carcinoma (NPC) prognostication. Recently, machine learning (ML) models are increasingly adopted for this purpose. However, only a few studies have compared the performances between CPH and ML models. This study aimed at comparing CPH with two state-of-the-art ML algorithms, namely, conditional survival forest (CSF) and DeepSurv for disease progression prediction in NPC. Methods: From January 2010 to March 2013, 412 eligible NPC patients were reviewed. The entire dataset was split into training cohort and testing cohort in a ratio of 90%:10%. Ten features from patient-related, disease-related, and treatment-related data were used to train the models for progression-free survival (PFS) prediction. The model performance was compared using the concordance index (c-index), Brier score, and log-rank test based on the risk stratification results. Results: DeepSurv (c-index = 0.68, Brier score = 0.13, log-rank test p = 0.02) achieved the best performance compared to CSF (c-index = 0.63, Brier score = 0.14, log-rank test p = 0.38) and CPH (c-index = 0.57, Brier score = 0.15, log-rank test p = 0.81). Conclusions: Both CSF and DeepSurv outperformed CPH in our relatively small dataset. ML-based survival prediction may guide physicians in choosing the most suitable treatment strategy for NPC patients.

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Shah, Syed Muazzam Ali, Semmy Wellem Taju, Bongani Brian Dlamini, and Yu-Yen Ou. "DeepSIRT: A deep neural network for identification of sirtuin targets and their subcellular localizations." Computational Biology and Chemistry 93 (August 2021): 107514. http://dx.doi.org/10.1016/j.compbiolchem.2021.107514.

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García-Guarín, Pedro Julián, Julián Cantor-López, Camilo Cortés-Guerrero, María Alejandra Guzmán-Pardo, and Sergio Rivera. "Implementación del algoritmo VNS-DEEPSO para el despacho de energía en redes distribuidas inteligentes." INGE CUC 15, no. 1 (June 8, 2019): 142–54. http://dx.doi.org/10.17981/ingecuc.15.1.2019.13.

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Introducción: Las redes eléctricas tradicionales están migrando a nuevas configuraciones de redes inteligentes, que traen retos operacionales y de planeación. Con miras a avanzar en estos retos se propone resolver un problema de optimización usando programación en elementos de redes distribuidas inteligentes. Objetivo: El problema de optimización consiste en administrar el despacho energético de una red inteligente para optimizar los recursos disponibles, considerando la incertidumbre de energías renovables, viajes planeados de vehículos eléctricos, el pronóstico de carga y los precios del mercado. Metodología: Se propuso utilizar un ensamble entre dos métodos heurísticos. El algoritmo VNS (Variable Neighborhood Search) y el DEEPSO (Differential Evolutionary Particle Swarm). Resultados: El algoritmo VNS-DEEPSO fue evaluado en una competencia de “Smart Grids” con otros algoritmos con un valor de 18.21, siendo 7 % mejor que el segundo algoritmo clasificado en la competencia. Conclusiones: El algoritmo VNS-DEEPSO fue ganador entre 9 algoritmos metaheurísticos que solucionaron el problema, este problema tenía un mayor incremento de dificultad debida a la incertidumbre generada por factores ambientales, pronóstico de carga, viajes en vehículos eléctricos y el mercado de precios. Acorde a los resultados, el algoritmo VNS-DEEPSO demostró ser el más eficiente en minimizar los costos operacionales y maximizar los ingresos de la red inteligente.

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Kong, Hongshan, and Bin Yu. "Modeling and Optimization of RFID Networks Planning Problem." Wireless Communications and Mobile Computing 2019 (December 7, 2019): 1–7. http://dx.doi.org/10.1155/2019/2745160.

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Aimed at solving the RFID networks planning problem, a mathematical model considering tag coverage and reader interference is presented. The DEEPSO algorithm that adds differential evolution and evolutionary strategies to the standard PSO is introduced to the optimization of RFID Networks Planning, which can improve the global convergence ability and particle diversity and can avoid falling into local convergence. According to the simulation results, compared with RFID networks planning by standard PSO, RFID networks planning by DEEPSO is superior.

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Lucas, John, Chih-Yang Hsu, Jared Becksfort, Scott Hwang, Zhaohua Lu, Yichuan Wang, Jason Chiang, et al. "IMG-20. RADIOMIC FEATURES IMPROVE PROGNOSTICATION OVER CONVENTIONAL MR DERIVED QUALITATIVE DESCRIPTORS IN PEDIATRIC SUPRATENTORIAL HIGH GRADE GLIOMA: COMPARISON OF MACHINE LEARNING TECHNIQUES." Neuro-Oncology 22, Supplement_3 (December 1, 2020): iii359. http://dx.doi.org/10.1093/neuonc/noaa222.355.

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Abstract PURPOSE/OBJECTIVES Pediatric supratentorial high-grade glioma (stHGG) is a biologically heterogeneous disease defined by unique mutations, natural history and prognosis. Prior work by our group outlined a role for qualitative imaging features in aiding prognostication. We build on that work by evaluating the prognostic utility of radiomic features (RM) when paired with clinical factors. MATERIALS/ METHODS Ninety-one patients age < 21 years with stHGG treated between 1980–2007 were retrospectively reviewed. Prognostic clinical, qualitative imaging (Visually AcceSAble Rembrandt Images, VASARI), and treatment characteristics were evaluated in concert with manual and automatically segmented (DeepMedic), tumor-derived semi-quantitative radiomic features (Pyradiomics) extracted from MR images. Prognostic RM were limited to stable imaging features which were subsequently selected using bootstrapped least absolute shrinkage and selection operator (LASSO). Nonparametric descriptive statistics and prognostication model evaluation, incorporating RM and clinical variables, were developed using random forest (RF), Cox proportional hazards (CPH), and deep learning (deepsurv) algorithms and assessed for goodness of fit using (c-index). RESULTS A subset (N=80) of 386 intensity, shape, and texture derived RM were stable between pre-treatment MR. 28 RM features were independently predictive of survival when compared to models utilizing combinations of clinical, VASARI and had comparable model fit statistics. CPH, RF and deepsurv showed comparable utility in modelling RM features. Combined modelling of clinical, VASARI and RM features using CPH, RF, and deepsurv resulted in c-indices of 0.68, 0.67, 0.68, respectively. CONCLUSION RM features are stable and independently prognostic. Combined modelling of clinical, VASARI, and RM features improves prognostication in stHGG.

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Puri, Nitish, Ashwani Jain, Piyush Mohanty, and Subhamoy Bhattacharya. "Earthquake Response Analysis of Sites in State of Haryana using DEEPSOIL Software." Procedia Computer Science 125 (2018): 357–66. http://dx.doi.org/10.1016/j.procs.2017.12.047.

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Khurana, Sameer, Reda Rawi, Khalid Kunji, Gwo-Yu Chuang, Halima Bensmail, and Raghvendra Mall. "DeepSol: a deep learning framework for sequence-based protein solubility prediction." Bioinformatics 34, no. 15 (March 15, 2018): 2605–13. http://dx.doi.org/10.1093/bioinformatics/bty166.

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Liu, Qun, Saikat Basu, Sangram Ganguly, Supratik Mukhopadhyay, Robert DiBiano, Manohar Karki, and Ramakrishna Nemani. "DeepSat V2: feature augmented convolutional neural nets for satellite image classification." Remote Sensing Letters 11, no. 2 (November 21, 2019): 156–65. http://dx.doi.org/10.1080/2150704x.2019.1693071.

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Yılmazoğlu, L., and G. Ç. İnce. "Investigation of the influence of topographic irregularities and two dimensional effects on the intensity of surface ground motion with one- and two-dimensional analyses." Natural Hazards and Earth System Sciences Discussions 1, no. 6 (December 6, 2013): 7193–238. http://dx.doi.org/10.5194/nhessd-1-7193-2013.

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Abstract. In this work, the surface ground motion that occurs during an earthquake in ground sections having different topographic forms has been examined with one and two dynamic site response analyses. One-dimensional analyses were undertaken using the Equivalent-Linear Earthquake Response Analysis program based on the equivalent linear analysis principle and the Deepsoil program which is able to make both equivalent linear and nonlinear analyses and two-dimensional analyses using the Plaxis software. The viscous damping parameters used in the dynamic site response analyses undertaken with the Plaxis software were obtained using the DeepSoil program. In the dynamic site response analyses, the synthetic acceleration over a 475 yr replication period representing the earthquakes in Istanbul was used as the basis of the bedrock ground motion. The peak ground acceleration obtained different depths of soils and acceleration spectrum values have been compared. The surface topography and layer boundaries in the 5-5' section were selected in order to examine the effect of the land topography and layer boundaries on the analysis results were flattened and compared with the actual status. The analysis results showed that the characteristics of the surface ground motion changes in relation to the varying local soil conditions and land topography.

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İnce, G. Ç., and L. Yılmazoğlu. "Investigating the influence of topographic irregularities and two-dimensional effects on surface ground motion intensity with one- and two-dimensional analyses." Natural Hazards and Earth System Sciences 14, no. 7 (July 18, 2014): 1773–88. http://dx.doi.org/10.5194/nhess-14-1773-2014.

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Abstract. In this work, the surface ground motion that occurs during an earthquake in ground sections having different topographic forms has been examined with one and two dynamic site response analyses. One-dimensional analyses were undertaken using the Equivalent-Linear Earthquake Response Analysis (EERA) program based on the equivalent linear analysis principle and the Deepsoil program which is able to make both equivalent linear and nonlinear analyses and two-dimensional analyses using the Plaxis 8.2 software. The viscous damping parameters used in the dynamic site response analyses undertaken with the Plaxis 8.2 software were obtained using the DeepSoil program. In the dynamic site response analyses, the synthetic acceleration over a 475-year return period representing the earthquakes in Istanbul was used as the basis of the bedrock ground motion. The peak ground acceleration obtained different depths of soils and acceleration spectrum values have been compared. The surface topography and layer boundaries in the 5-5' cross section which cuts across the study area west to east were selected in order to examine the effect of the land topography and layer boundaries on the analysis results, and were flattened and compared with the actual status. The analysis results showed that the characteristics of the surface ground motion change in relation to the varying local soil conditions and land topography.

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Rivera, Sergio, and Jefferson Torres. "Optimal energy dispatch in multiple periods of time considering the variability and uncertainty of generation from renewable sources/Despacho de energía óptimo en múltiples periodos de tiempo considerando la variabilidad y la incertidumbre de la genera..." Prospectiva 16, no. 2 (August 1, 2018): 75–81. http://dx.doi.org/10.15665/rp.v16i2.1642.

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En este documento se especificarán los resultados obtenidos al desarrollar una función de optimización de costos para el despacho de energía en múltiples (6) periodos de tiempo considerando la variabilidad y la incertidumbre de diversas fuentes de generación de energías renovables como lo son el sol, el viento, además de los ciclos de carga de Vehículos Eléctricos (VE) en una red de generación distribuida. El algoritmo de optimización heurístico utilizado fue tipo DEEPSO (combinación de enjambre de partículas y evolución diferencial) en el cual se tuvo en cuenta costos de penalización por la sub o sobre estimación del potencial energético, penalizaciones por limitaciones físicas del sistema, además de la optimización del flujo de potencia mediante la inyección de reactivos con capacitores tipo shunt o mediante la utilización de taps de transformadores. El resultado final de la investigación fue el desarrollo de un algoritmo tipo DEEPSO para la optimización del despacho económico de energía en sistemas de potencia con alta penetración de fuentes renovables.

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Stegmayer, Georgina, Cristian Yones, Laura Kamenetzky, and Diego H. Milone. "High Class-Imbalance in pre-miRNA Prediction: A Novel Approach Based on deepSOM." IEEE/ACM Transactions on Computational Biology and Bioinformatics 14, no. 6 (November 1, 2017): 1316–26. http://dx.doi.org/10.1109/tcbb.2016.2576459.

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Naveem, B. P., T. G. Sitharam, and P. V. Sivapullaiah. "Seismic Analysis of Municipal Solid Waste Landfill in India." International Journal of Geotechnical Earthquake Engineering 6, no. 2 (July 2015): 35–55. http://dx.doi.org/10.4018/ijgee.2015070103.

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This paper presents, unit weight, shear wave velocity, strain-dependent normalized shear modulus reduction and material damping ratio relationships for Mavallipura landfill are developed based on field testing, laboratory measurements and also validated using semi-empirical methods. In addition, one-dimensional seismic response analysis by an equivalent linear method for Mavallipura landfill, Bangalore using the software like SHAKE2000 and DEEPSOIL. Results indicated that the MSW landfill has less shear stiffness and more amplification due to the loose filling and damping, which need to be accounted for seismically safe MSW landfill design in India.

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Liu, Yan, Lina Yao, Bin Guo, Nuo Li, Jing Zhang, Jingmin Chen, Daqing Zhang, Yinxiao Liu, Zhiwen Yu, and Sizhe Zhang. "DeepStore: An Interaction-Aware Wide&Deep Model for Store Site Recommendation With Attentional Spatial Embeddings." IEEE Internet of Things Journal 6, no. 4 (August 2019): 7319–33. http://dx.doi.org/10.1109/jiot.2019.2916143.

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Touil, N., A. Khamlichi, P. Dubujet, and A. Jabbouri. "Predicting liquefaction of tangier soils by using semiempirical approaches and continuum based modelling." International Review of Applied Sciences and Engineering 3, no. 2 (December 1, 2012): 119–26. http://dx.doi.org/10.1556/irase.3.2012.2.5.

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Abstract Liquefaction potential of soils under the risk of seism is usually assessed by using correlation formulas that are based on field tests and historical earthquakes databases. These correlations depend on the site where they were derived. To use them for other sites where seismic history is not available, further investigation is needed. In this work, a one-dimensional modelling of liquefaction phenomenon is performed by using DeepSoil software. The soil data required for simulations were obtained from field tests consisting of core sampling and cone penetration testing. Using reliability analysis, the probability of liquefaction was estimated for sandy soils located in the Moroccan city of Tangier. The obtained results were found to be close to predictions due to Juang semiempirical approach.

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Yoshida, Hotaka, and Yoshikazu Fukuyama. "Dependable Parallel Multi-Swarm C-DEEPSO with Migration for Voltage and Reactive Power Control." IFAC-PapersOnLine 52, no. 4 (2019): 18–23. http://dx.doi.org/10.1016/j.ifacol.2019.08.148.

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Jin, Yong, Hoyeon Kim, Daehyeon Kim, Yonghee Lee, and Haksung Kim. "Seismic Response of Flat Ground and Slope Models through 1 g Shaking Table Tests and Numerical Analysis." Applied Sciences 11, no. 4 (February 20, 2021): 1875. http://dx.doi.org/10.3390/app11041875.

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In order to verify the reliability of numerical analysis, a series of 1 g shaking table tests for flat ground and slope were conducted using a laminar shear box subjected to different seismic waves. Firstly, numerical analyses, using the DEEPSOIL and ABAQUS software, were done to compare the results of flat ground experiments. After that, finite element analyses with ABAQUS were conducted to compare the results of slope experiments. For numerical analyses, considering the influence of the boundary, the concept of adjusted elastic modulus was proposed to improve the simulation results. Based on the analyses, it is found that in terms of acceleration-time history and spectral acceleration, the numerical analysis results are in good agreement with the experiment results. This implies that numerical analysis can capture the dynamic behavior of soil under 1 g shaking table test conditions.

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Acosta Piedrahita, Jorge Luis, Angel Estefano Alarcon Roa, and Sergio Rivera Rodríguez. "Reconfiguración de sistemas de distribución para minimizar pérdidas utilizando optimización heurística: Métodos BPSO y DEEPSO." Entre ciencia e ingeniería 11, no. 22 (November 30, 2017): 110. http://dx.doi.org/10.31908/19098367.3556.

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Garcia-Guarin, Julian, Diego Rodriguez, David Alvarez, Sergio Rivera, Camilo Cortes, Alejandra Guzman, Arturo Bretas, Julio Romero Aguero, and Newton Bretas. "Smart Microgrids Operation Considering a Variable Neighborhood Search: The Differential Evolutionary Particle Swarm Optimization Algorithm." Energies 12, no. 16 (August 16, 2019): 3149. http://dx.doi.org/10.3390/en12163149.

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Increased use of renewable energies in smart microgrids (SMGs) present new technical challenges to system operation. SMGs must be self-sufficient and operate independently; however, when more elements are integrated into SMGs, as distributed energy resources (DER), traditional explicit mathematical formulations will demand too much data from the network and become intractable. In contrast, tools based on optimization with metaheuristics can provide near optimal solutions in acceptable times. Considering this, this paper presents the variable neighborhood search differential evolutionary particle swarm optimization (VNS-DEEPSO) algorithm to solve multi-objective stochastic control models, as SMGs system operation. The goal is to control DER while maximizing profit. In this work, DER considered the bidirectional communication between energy storage systems (ESS) and electric vehicles (EVs). They can charge/discharge power and buy/sell energy in the electricity markets. Also, they have elements such as traditional generators (e.g., reciprocating engines) and loads, with demand response/control capability. Sources of uncertainty are associated with weather conditions, planned EV trips, load forecasting and the market prices. The VNS-DEEPSO algorithm was the winner of the IEEE Congress on Evolutionary Computation/The Genetic and Evolutionary Computation Conference (IEEE-CEC/GECCO 2019) smart grid competition (with encrypted code) and also won the IEEE World Congress on Computational Intelligence (IEEE-WCCI) 2018 smart grid competition (these competitions were developed by the group GECAD, based at the Polytechnic Institute of Porto, in collaboration with Delft University and Adelaide University). In the IEEE-CEC/GECCO 2019, the relative error improved between 32% and 152% in comparison with other algorithms.

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Banks, Jonathan, Spencer Poulette, Jens Grimmer, Florian Bauer, and Eva Schill. "Geochemical Changes Associated with High-Temperature Heat Storage at Intermediate Depth: Thermodynamic Equilibrium Models for the DeepStor Site in the Upper Rhine Graben, Germany." Energies 14, no. 19 (September 24, 2021): 6089. http://dx.doi.org/10.3390/en14196089.

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The campus of the Karlsruhe Institute of Technology (KIT) contains several waste heat streams. In an effort to reduce greenhouse gas emissions by optimizing thermal power consumption on the campus, researchers at the KIT are proposing a ‘DeepStor’ project, which will sequester waste heat from these streams in an underground reservoir during the summer months, when the heat is not required. The stored heat will then be reproduced in the winter, when the campus’s thermal power demand is much higher. This paper contains a preliminary geochemical risk assessment for the operation of this subsurface, seasonal geothermal energy storage system. We used equilibrium thermodynamics to determine the potential phases and extent of mineral scale formation in the plant’s surface infrastructure, and to identify possible precipitation, dissolution, and ion exchange reactions that may lead to formation damage in the reservoir. The reservoir in question is the Meletta Beds of the Upper Rhein Graben’s Froidefontaine Formation. We modeled scale- and formation damage-causing reactions during six months of injecting 140 °C fluid into the reservoir during the summer thermal storage season and six months of injecting 80 °C fluid during the winter thermal consumption season. Overall, we ran the models for 5 years. Anhydrite and calcite are expected mineral scales during the thermal storage season (summer). Quartz is the predicted scale-forming mineral during the thermal consumption period (winter). Within ~20 m of the wellbores, magnesium and iron are leached from biotite; calcium and magnesium are leached from dolomite; and sodium, aluminum, and silica are leached from albite. These reactions lead to a net increase in both porosity and permeability in the wellbore adjacent region. At a distance of ~20–75 m from the wellbores, the leached ions recombine with the reservoir rocks to form a variety of clays, i.e., saponite, minnesotaite, and daphnite. These alteration products lead to a net loss in porosity and permeability in this zone. After each thermal storage and production cycle, the reservoir shows a net retention of heat, suggesting that the operation of the proposed DeepStor project could successfully store heat, if the geochemical risks described in this paper can managed.

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Iswanto, Eko Rudi, and Eric Yee. "COMPARISON OF EQUIVALENT LINEAR AND NON LINEAR METHODS ON GROUND RESPONSE ANALYSIS: CASE STUDY AT WEST BANGKA SITE." Jurnal Pengembangan Energi Nuklir 18, no. 1 (October 20, 2016): 23. http://dx.doi.org/10.17146/jpen.2016.18.1.2994.

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COMPARISON OF EQUIVALENT LINEAR AND NON LINEAR METHODS ON GROUND RESPONSE ANALYSIS: CASE STUDY AT WEST BANGKA SITE. Within the framework of identifying NPP sites, site surveys are performed in West Bangka (WB), Bangka-Belitung Island Province. Ground response analysis of a potential site has been carried out using peak strain profiles and peak ground acceleration. The objective of this research is to compare Equivalent Linear (EQL) and Non Linear (NL) methods of ground response analysis on the selected NPP site (West Bangka) using DeepSoil software. Equivalent linear method is widely used because requires soil data in simple way and short time of computational process. On the other hand, non linear method is capable of representing the actual soil behaviour by considering non linear soil parameter. The results showed that EQL method has similar trends to NL method. At surface layer, the acceleration values for EQL and NL methods are resulted as 0.425g and 0.375g respectively. NL method is more reliable in capturing higher frequencies of spectral acceleration compared to EQL method.

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Reddy, M. V. Ravi Kishore, Supriya Mohanty, and Rehana Shaik. "Seismic Performance of Soil-Ash and Soil-Ash-Foundation System." International Journal of Geotechnical Earthquake Engineering 11, no. 1 (January 2020): 45–70. http://dx.doi.org/10.4018/ijgee.2020010103.

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In this study, a 3D seismic response of soil deposit, soil-ash deposit and soil-ash-foundation system was investigated. Homogeneous sand deposit of 80m × 9m × 20m was initially analyzed. A pond ash layer is on top of the sand deposit with varying thicknesses and the efficiency of the pond ash layer on the sand deposit was evaluated for its best suitability. The optimum sand-ash deposit overlain by a shallow foundation has been analysed under the excitation of the Nepal (Mw:7.8) and North East India earthquake (Mw:7.5). A seismic response analysis was performed using finite element software PLAXIS3D. The finite element model adopted for the present study has been validated using 1D nonlinear ground response analysis programs. e.g. DEEPSOIL and Cyclic1D. Results of the response analysis have been determined in terms of acceleration, displacement, excess pore pressure, and excess pore pressure ratio. It was observed that, the sand-pond ash-foundation system experienced liquefaction when excited under the Nepal earthquake motion whereas it is safe against the North East India earthquake.

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Das, Angshuman, and Pradipta Chakrabortty. "ONE-DIMENSIONAL SEISMIC ENERGY TRANSMISSION ALONG HETEROGENEOUS LAYERED SOIL." International Journal of Students' Research in Technology & Management 4, no. 3 (December 2, 2016): 49. http://dx.doi.org/10.18510/ijsrtm.2016.432.

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In this present study an initiative has been taken to find out the modification in the seismic energy along distance its travel because of the soil heterogeneity. Soil heterogeneity is considered here in one dimensional analysis and analyses were performed using software DEEPSOIL. Both equivalent linear and nonlinear analyses were performed on homogenous and heterogeneous soil models: uniform loose sand, uniform soft clay and layered soil deposit of sandwiched clay layer between loose sandy soils. The performances of these soil models are compared here in terms of peak ground acceleration (PGA) value, and seismic energy migration in terms of Arias Intensity (AI) evolution along the depth inside the soil deposit. It is observed from the analysis that, less seismic energy and PGA is developed in the heterogeneous soil than that in homogeneous soil. This is because during earthquake more softening is taking place in the layered soil than that in uniform soil. Further in this paper the requirement of nonlinear analysis over the equivalent linear analysis is also presented.

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Noto, Davide, Antonina Giammanco, Rossella Spina, Francesca Fayer, Angelo B. Cefalù, and Maurizio R. Averna. "DeepSRE: Identification of sterol responsive elements and nuclear transcription factors Y proximity in human DNA by Convolutional Neural Network analysis." PLOS ONE 16, no. 3 (March 4, 2021): e0247402. http://dx.doi.org/10.1371/journal.pone.0247402.

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SREBP1 and 2, are cholesterol sensors able to modulate cholesterol-related gene expression responses. SREBPs binding sites are characterized by the presence of multiple target sequences as SRE, NFY and SP1, that can be arranged differently in different genes, so that it is not easy to identify the binding site on the basis of direct DNA sequence analysis. This paper presents a complete workflow based on a one-dimensional Convolutional Neural Network (CNN) model able to detect putative SREBPs binding sites irrespective of target elements arrangements. The strategy is based on the recognition of SRE linked (less than 250 bp) to NFY sequences according to chromosomal localization derived from TF Immunoprecipitation (TF ChIP) experiments. The CNN is trained with several 100 bp sequences containing both SRE and NF-Y. Once trained, the model is used to predict the presence of SRE-NFY in the first 500 bp of all the known gene promoters. Finally, genes are grouped according to biological process and the processes enriched in genes containing SRE-NFY in their promoters are analyzed in details. This workflow allowed to identify biological processes enriched in SRE containing genes not directly linked to cholesterol metabolism and possible novel DNA patterns able to fill in for missing classical SRE sequences.

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Gensheimer, Michael F., and Balasubramanian Narasimhan. "A scalable discrete-time survival model for neural networks." PeerJ 7 (January 25, 2019): e6257. http://dx.doi.org/10.7717/peerj.6257.

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There is currently great interest in applying neural networks to prediction tasks in medicine. It is important for predictive models to be able to use survival data, where each patient has a known follow-up time and event/censoring indicator. This avoids information loss when training the model and enables generation of predicted survival curves. In this paper, we describe a discrete-time survival model that is designed to be used with neural networks, which we refer to as Nnet-survival. The model is trained with the maximum likelihood method using mini-batch stochastic gradient descent (SGD). The use of SGD enables rapid convergence and application to large datasets that do not fit in memory. The model is flexible, so that the baseline hazard rate and the effect of the input data on hazard probability can vary with follow-up time. It has been implemented in the Keras deep learning framework, and source code for the model and several examples is available online. We demonstrate the performance of the model on both simulated and real data and compare it to existing models Cox-nnet and Deepsurv.

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Baron, Carlo, Ameena S. Al-Sumaiti, and Sergio Rivera. "Impact of Energy Storage Useful Life on Intelligent Microgrid Scheduling." Energies 13, no. 4 (February 20, 2020): 957. http://dx.doi.org/10.3390/en13040957.

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Planning the operation scheduling with optimization heuristic algorithms allows microgrids to have a convenient tool. The developments done in this study attain this scheduling taking into account the impact of energy storage useful life in the microgrid operation. The scheduling solutions, proposed for the answer of an optimization problem, are obtained by using a metaheuristic algorithm called Differential Evolutionary Particle Swarm Optimization (DEEPSO). Thanks to the optimization that is conducted in this study, it is possible to formulate dispatches of the existent microgrid (MG) by always looking for the ideal dispatch that implies a lower cost and provides a greater viability to any project related to renewable energy, electric vehicles and energy storage. These advances oblige the battery manufacturers to start looking for more powerful batteries, with lower costs and longer useful life. In this way, this paper proposes a scheduling tool considering the energy storage useful life.

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Torres Rivero, Jefferson, and Sergio Rivera. "Despacho de energía óptimo en múltiples periodos considerando la incertidumbre de la generación a partir de fuentes renovables en un modelo reducido del sistema de potencia colombiano." AVANCES: Investigación en Ingeniería 15, no. 1 (December 24, 2018): 48–58. http://dx.doi.org/10.18041/1794-4953/avances.1.4732.

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En este artículo se presentan los resultados de la programación de la operación obtenidos al aplicar una función objetivo de optimización de costos de incertidumbre para el despacho de energía en varios periodos. Estos se obtuvieron considerando la distribución de probabilidad e incertidumbre tanto de diversas fuentes de generación de energías renovables (solar y eólica) como la gestión de vehículos eléctricos (VE) en un modelo reducido del sistema de potencia colombiano. El algoritmo de optimización heurístico utilizado fue uno de tipo DEEPSO (combinación de enjambre de partículas y evolución diferencial) en que se tuvo en cuenta factores como costos de penalización por la sub o sobreestimación del potencial energético; penalizaciones por limitaciones físicas del sistema como tensiones máximas de los nodos y corrientes máximas de las líneas; optimización del flujo de potencia mediante la inyección de reactivos con capacitores tipo shunt y la utilización de taps de transformadores.

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Méndez, Carlos, Jerson Mejía, Sergio Rivera, and Gustavo Coria. "Estrategia de carga inteligente de vehículos eléctricos para múltiples agregadores, utilizando optimización heurística." Journal de Ciencia e Ingeniería 12, no. 1 (August 31, 2020): 20–32. http://dx.doi.org/10.46571/jci.2020.1.3.

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El presente artículo propone una solución, mediante métodos heurísticos, al problema de carga de 150 vehículos eléctricos, a través de 32 agregadores dependientes de un transformador principal. La solución propuesta esté basada en estudios realizados con anterioridad para un método de optimización analítica, que funciona hasta con un máximo de 7 agregadores. Esta solución se logra mediante el uso de ecuaciones lineales que limitan el costo y la carga disponible para el sistema, mediante el uso de la función “fmincom” del software Matlab®, que utiliza métodos de solución analíticos de optimización. Adicionalmente, se realiza una estrategia de optimización heurística basada en el método DEEPSO (combinación de enjambre de partículas y evolución diferencial) y usando las mismas restricciones pero verificando que la estrategia cumpla el objetivo propuesto. Se muestran gráficas de los resultados obtenidos en la optimización, y se realiza un análisis comparativo de los métodos.

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Bhutani, Manish, and Sanjeev Naval. "Assessment of Seismic Site Response and Liquefaction Potential for Some Sites using Borelog Data." Civil Engineering Journal 6, no. 11 (November 1, 2020): 2103–19. http://dx.doi.org/10.28991/cej-2020-03091605.

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Assessment of Liquefaction susceptibility of soil is very important aspect of disaster risk reduction for a particular region. The present research is an investigation to find out the liquefaction capability for the sites of Jalandhar and its surrounding region, Punjab (India) using semi empirical approach of Idris and Boulanger. Initially, the response of Ground has been analyzed with the help of DEEPSOIL software for evaluating the maximum ground acceleration values (PGASUR) at surface using five earthquake motions of magnitude, M = 6.0, 6.8 and 7.3 selected from worldwide recorded database based on seismicity of the region. The investigated PGA values ranges from 0.196 g to 0.292 g for the sites under investigation. Soil’s potential against liquefaction for 45 locations has been carried out using PGASUR results so obtained. It has been observed that eighteen sites out of forty-five are found to be susceptible to liquefaction. In order to help structural designers and geotechnical engineers for the preparation of realistic plan towards disaster risk reduction for the region, PGASUR contour map of obtained results and liquefaction hazard maps for earthquake of magnitude 6.0 and 7.0 has been prepared on geographical information system (GIS) platform using QGIS software. Doi: 10.28991/cej-2020-03091605 Full Text: PDF

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Jomaa, Hadi S., Lars Schmidt-Thieme, and Josif Grabocka. "Dataset2Vec: learning dataset meta-features." Data Mining and Knowledge Discovery 35, no. 3 (February 25, 2021): 964–85. http://dx.doi.org/10.1007/s10618-021-00737-9.

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AbstractMeta-learning, or learning to learn, is a machine learning approach that utilizes prior learning experiences to expedite the learning process on unseen tasks. As a data-driven approach, meta-learning requires meta-features that represent the primary learning tasks or datasets, and are estimated traditonally as engineered dataset statistics that require expert domain knowledge tailored for every meta-task. In this paper, first, we propose a meta-feature extractor called Dataset2Vec that combines the versatility of engineered dataset meta-features with the expressivity of meta-features learned by deep neural networks. Primary learning tasks or datasets are represented as hierarchical sets, i.e., as a set of sets, esp. as a set of predictor/target pairs, and then a DeepSet architecture is employed to regress meta-features on them. Second, we propose a novel auxiliary meta-learning task with abundant data called dataset similarity learning that aims to predict if two batches stem from the same dataset or different ones. In an experiment on a large-scale hyperparameter optimization task for 120 UCI datasets with varying schemas as a meta-learning task, we show that the meta-features of Dataset2Vec outperform the expert engineered meta-features and thus demonstrate the usefulness of learned meta-features for datasets with varying schemas for the first time.

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Yu, Hao, Eric Ntambakwa, and Bruno Mendes. "Comparison of 1-D seismic site response analysis tools for layered liquefiable deposits at an offshore windfarm site." E3S Web of Conferences 205 (2020): 12005. http://dx.doi.org/10.1051/e3sconf/202020512005.

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Various offshore wind farms have been proposed in the Taiwan strait with a long-term target of installing 4.2 gigawatts by 2030. The proposed projects will be in areas with various known faults and areal seismic sources which should be accounted for in design. A reliable prediction of site response for soil deposits is crucial for seismic loading evaluation of existing energy structures and for design of new structures including offshore wind farms in seismically active regions. This paper presents generic results of 1-D site response analyses based on work performed for an offshore wind power plant development site in the Taiwan strait. The deposits in the project area generally consist of layered deposits with liquefiable layers. The site response analyses were initially performed using two different open-source tools, DEEPSOIL and Cyclic 1D. Both equivalent linear and non-linear approaches were adopted for the analyses and additional evaluations were subsequently performed using PLAXIS 2D for comparison with the results from the open source tools. The results from the different tools were systematically compared and provided useful insight on peak ground (seabed) acceleration, acceleration time histories and shear strains at specific depths and design response spectra. The paper includes a discussion of the sensitivity of the outputs to various input parameters for each of the tools utilized in the analyses and the suitability and limitations of each approach for assessing liquefaction potential are also discussed.

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Osório, Gerardo, Mohamed Lotfi, Miadreza Shafie-khah, Vasco Campos, and João Catalão. "Hybrid Forecasting Model for Short-Term Electricity Market Prices with Renewable Integration." Sustainability 11, no. 1 (December 22, 2018): 57. http://dx.doi.org/10.3390/su11010057.

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In recent years, there have been notable commitments and obligations by the electricity sector for more sustainable generation and delivery processes to reduce the environmental footprint. However, there is still a long way to go to achieve necessary sustainability goals while ensuring standards of robustness and the quality of power grids. One of the main challenges hindering this progress are uncertainties and stochasticity associated with the electricity sector and especially renewable generation. In this paradigm shift, forecasting tools are indispensable, and their utilization can significantly improve system operation and minimize costs associated with all related activities. Thus, forecasting tools have an essential key role in all decision-making stages. In this work, a hybrid probabilistic forecasting model (HPFM) was developed for short-term electricity market prices (EMP) combining wavelet transforms (WT), hybrid particle swarm optimization (DEEPSO), adaptive neuro-fuzzy inference system (ANFIS), and Monte Carlo simulation (MCS). The proposed hybrid probabilistic forecasting model (HPFM) was tested and validated with real data from the Spanish and Pennsylvania-New Jersey-Maryland (PJM) markets. The proposed model exhibited favorable results and performance in comparison with previously published work considering electricity market prices (EMP) data, which is notable.

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Iwata, Sohei, and Yoshikazu Fukuyama. "Dependability Evaluations of Parallel Differential Evolutionary Particle Swarm Optimization for Voltage and Reactive Power Control." IEEJ Transactions on Power and Energy 137, no. 1 (2017): 52–58. http://dx.doi.org/10.1541/ieejpes.137.52.

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Guzman, Wilder, Sebastián Osorio, and Sergio Rivera. "Modelado de cargas controlables en el despacho de sistemas con fuentes renovables y vehículos eléctricos." Ingeniería y Región 17 (June 30, 2017): 49. http://dx.doi.org/10.25054/22161325.1535.

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El presente documento muestra el despacho económico realizado en un sistema de potencia con penetración de fuentes renovables, carros eléctricos y con especial énfasis en el modelado de cargas controlables. Para ello, se estudió el comportamiento de la irradiancia solar, la velocidad del viento y los patrones de conducción de carros eléctricos por medio de distribuciones de probabilidad Log-Normal, Weibull y Normal, respectivamente. Se definió el concepto de carga controlable, así como los requisitos del contrato con el operador de red para que un centro de consumo pueda declararse como controlable y se utilizó un modelo de minimización de costos de compensación por bloque de potencia no despachada para modelar el comportamiento energético-económico de dichos nodos desde el punto de vista del operador de red. La optimización del despacho (flujo óptimo de potencia) se hizo por medio del algoritmo de optimización DEEPSO mediante la inclusión de 7 nodos controlables, escogidos en base a un criterio de selección establecido. Se encontró que las cargas controlables pueden presentar dos grandes beneficios para el sistema dependiendo de los parámetros establecidos en el contrato: suavización del perfil de demanda (desplazamiento de picos de máxima potencia y disminución de pérdidas) y disminución del costo total de generación.

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Sato, Mayuko, Yoshikazu Fukuyama, Tatsuya Iizaka, and Tetsuro Matsui. "Total Optimization of Energy Networks in a Smart City by Multi-Population Global-Best Modified Brain Storm Optimization with Migration." Algorithms 12, no. 1 (January 7, 2019): 15. http://dx.doi.org/10.3390/a12010015.

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This paper proposes total optimization of energy networks in a smart city by multi-population global-best modified brain storm optimization (MP-GMBSO). Efficient utilization of energy is necessary for reduction of CO2 emission, and smart city demonstration projects have been conducted around the world in order to reduce total energies and the amount of CO2 emission. The problem can be formulated as a mixed integer nonlinear programming (MINLP) problem and various evolutionary computation techniques such as particle swarm optimization (PSO), differential evolution (DE), Differential Evolutionary Particle Swarm Optimization (DEEPSO), Brain Storm Optimization (BSO), Modified BSO (MBSO), Global-best BSO (BSO), and Global-best Modified Brain Storm Optimization (GMBSO) have been applied to the problem. However, there is still room for improving solution quality. Multi-population based evolutionary computation methods have been verified to improve solution quality and the approach has a possibility for improving solution quality. The proposed MS-GMBSO utilizes only migration for multi-population models instead of abest, which is the best individual among all of sub-populations so far, and both migration and abest. Various multi-population models, migration topologies, migration policies, and the number of sub-populations are also investigated. It is verified that the proposed MP-GMBSO based method with ring topology, the W-B policy, and 320 individuals is the most effective among all of multi-population parameters.

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Yoshida, Hotaka, and Yoshikazu Fukuyama. "Parallel Multi-population Differential Evolutionary Particle Swarm Optimization for Voltage and Reactive Power Control." IEEJ Transactions on Power and Energy 138, no. 2 (2018): 116–23. http://dx.doi.org/10.1541/ieejpes.138.116.

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Nishimura, Norihiro, Yoshikazu Fukuyama, and Tetsuro Matsui. "Dependable Parallel Differential Evolutionary Particle Swarm Optimization for On-line Optimal Operational Planning of Energy Plants." IEEJ Transactions on Electronics, Information and Systems 137, no. 11 (2017): 1479–87. http://dx.doi.org/10.1541/ieejeiss.137.1479.

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Journal articles on the topic 'DeepSORT'

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