scholarly journals ESTIMASI PARAMETER POPULASI DAN RASIO POTENSI PEMIJAHAN TONGKOL KOMO (Euthynnus affinis, Cantor 1849) DI PERAIRAN SELATAN LOMBOK

2020 ◽  
Vol 26 (2) ◽  
pp. 93
Author(s):  
Arief Wujdi ◽  
Hety Hartaty ◽  
Bram Setyadji
Keyword(s):  
Time Series Data ◽  
Measurement Data ◽  
Series Data ◽  
Small Scale ◽  
Von Bertalanffy ◽  
Fishing Pressure ◽  
Population Parameters ◽  
Important Species ◽  
Exploitation Rate ◽  
Euthynnus Affinis

Tongkol komo (Euthynnus affinis) merupakan salah satu komoditas ekonomis tinggi perikanan tuna neritik, terutama bagi armada tuna skala kecil. Seiring dengan meningkatnya intensitas penangkapan pada satu dekade terakhir, diperlukan kajian kuantitatif terkait keberlangsungan stok. Akan tetapi, minimnya data yang tersedia pada perikanan jenis ini merupakan tantangan terbesar dalam melakukan usaha pengelolaan. Penelitian ini bertujuan untuk menduga parameter populasi dan rasio potensi pemijahan (SPR) berbasis ukuran panjang, sebagai titik acuan biologis kondisi stok dalam menghadapi tekanan penangkapan. Total 1.321 data ukuran panjang dikumpulkan secara acak setiap bulan selama Januari hingga Desember 2016 di Tanjung Luar. Parameter populasi meliputi pertumbuhan, kematian, rekrutmen, dan laju pemanfaatan diestimasi dengan metode ELEFAN. Analisis SPR juga dilakukan dengan melibatkan parameter reproduksi yang disintesis dari penelitian sebelumnya dengan paket LB-SPR. Hasil penelitian menunjukkan bahwa formula pertumbuhan von Bertalanffy diekspresikan dengan persamaan Lt = 85,0 (1-e-0,7 (t+0,173)). Meskipun rata-rata sampel tongkol komo diprediksi telah matang gonad/memijah (SL50>L50), namun sumberdaya tongkol komo mengalami tekanan yang tergolong tinggi dan mengganggu rekrutmen individu baru ke dalam stok yang diindikasikan dengan parameter lainnya seperti rasio mortalitas penangkapan relatif (F/M) = 2,15, laju eksploitasi (E) = 0,68, dan SPR = 23%. Oleh karena itu, diperlukan penyusunan pengelolaan yang efektif untuk kelestarian perikanan. Eastern little tuna or commercially known as kawakawa is listed as one of the most economically important species of neritic tuna, especially caught by small-scale tuna fisheries. Increasing fishing pressure in the last decade should be responded by a quantitative analysis on its stock. The problem arises when this typical fishery usually possesses limited time-series data. This study intended to estimate population parameters and spawning potential ratio (SPR) as a biological reference point to state the healthiness of fishery corresponding to the fishing pressure around coastal areas. A total of 1,321 length measurement data were  randomly sampled monthly from January to December 2016 in Tanjung Luar. Population parameters including growth, mortality, recruitment, and exploitation rate were estimated by applying the ELEFAN method. SPR analysis was also carried out by involving reproduction parameters synthesized from previous studies using the LBSPR package. The results showed that the von Bertalanffy growth functions were expressed by the equation Lt = 85.0 (1-e-0.7(t + 0.173)). Although the majority of kawakawa was predicted in maturity as indicated by SL50>L50, high exploitation has occurred to the fishery that can be interfered the recruitment to the stock, as confirmed by other parameters, such as relative fishing mortality (F/M) = 2.15, exploitation rate (E) = 0.68, and SPR = 23%. Hence, the establishment of appropriate management strategies is needed to aim fishery sustainability. 

Causation Entropy Identifies Sparsity Structure for Parameter Estimation of Dynamic Systems

2016 ◽  
Vol 12 (1) ◽  
Author(s):  
Pileun Kim ◽  
Jonathan Rogers ◽  
Jie Sun ◽  
Erik Bollt
Keyword(s):  
Parameter Estimation ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Measurement Data ◽  
Relative Magnitude ◽  
Initial Guess ◽  
Series Data ◽  
System Parameters ◽  
Estimation Problems ◽  
System States

Parameter estimation is an important topic in the field of system identification. This paper explores the role of a new information theory measure of data dependency in parameter estimation problems. Causation entropy is a recently proposed information-theoretic measure of influence between components of multivariate time series data. Because causation entropy measures the influence of one dataset upon another, it is naturally related to the parameters of a dynamical system. In this paper, it is shown that by numerically estimating causation entropy from the outputs of a dynamic system, it is possible to uncover the internal parametric structure of the system and thus establish the relative magnitude of system parameters. In the simple case of linear systems subject to Gaussian uncertainty, it is first shown that causation entropy can be represented in closed form as the logarithm of a rational function of system parameters. For more general systems, a causation entropy estimator is proposed, which allows causation entropy to be numerically estimated from measurement data. Results are provided for discrete linear and nonlinear systems, thus showing that numerical estimates of causation entropy can be used to identify the dependencies between system states directly from output data. Causation entropy estimates can therefore be used to inform parameter estimation by reducing the size of the parameter set or to generate a more accurate initial guess for subsequent parameter optimization.

scholarly journals Lagrangian and spectral analysis of the forced flow past a circular cylinder using pulsed tangential jets

2012 ◽  
Vol 696 ◽  
pp. 285-300 ◽  
Author(s):  
T. Jardin ◽  
Y. Bury
Keyword(s):  
Circular Cylinder ◽  
Time Series Data ◽  
Excitation Frequency ◽  
Shear Layers ◽  
Forced Flow ◽  
Series Data ◽  
Small Scale ◽  
Characteristic Time Scale ◽  
Momentum Coefficient ◽  
Flow Past

AbstractWe numerically investigate the influence of pulsed tangential jets on the flow past a circular cylinder. To this end a spectral-Lagrangian dual approach is used on the basis of time-series data. The analysis reveals that the flow response to unsteady forcing is driven by strong interactions between shear layers and pulsed jets. The latter preferentially lead to either the lock-on regime or the quasi-steady vortex feeding regime whether the excitation frequency is of the order of, or significantly greater than, the frequency of the natural instability. The intensity of the wake vortices is mainly influenced by the momentum coefficient through the introduction of opposite-sign vorticity in the shear layers. This feature is emphasized using a modal-based time reconstruction, i.e. by reconstructing the flow field upon a specific harmonic spectrum associated with a characteristic time scale. The quasi-steady regime exhibits small-scale counter-rotating vortices that circumscribe the separated region. In the lock-on regime, atypical wake patterns such as 2P or $\mathrm{P} + \mathrm{S} $ can be observed, depending on the forcing frequency and the momentum coefficient, highlighting remarkable analogies with oscillating cylinders.

scholarly journals Forecast Quality of Physics-Based and Data-Driven PV Performance Models for a Small-Scale PV System

2021 ◽  
Vol 9 ◽  
Author(s):  
Moritz Stüber ◽  
Felix Scherhag ◽  
Matthieu Deru ◽  
Alassane Ndiaye ◽  
Muhammad Moiz Sakha ◽  
...  
Keyword(s):  
Smart Grids ◽  
Time Series Data ◽  
Short Term Memory ◽  
Data Driven ◽  
Series Data ◽  
Small Scale ◽  
Pv System ◽  
Advantages And Disadvantages ◽  
Forecast Quality

In the context of smart grids, the need for forecasts of the power output of small-scale photovoltaic (PV) arrays increases as control processes such as the management of flexibilities in the distribution grid gain importance. However, there is often only very little knowledge about the PV systems installed: even fundamental system parameters such as panel orientation, the number of panels and their type, or time series data of past PV system performance are usually unknown to the grid operator. In the past, only forecasting models that attempted to account for cause-and-effect chains existed; nowadays, also data-driven methods that attempt to recognize patterns in past behavior are available. Choosing between physics-based or data-driven forecast methods requires knowledge about the typical forecast quality as well as the requirements that each approach entails. In this contribution, the achieved forecast quality for a typical scenario (day-ahead, based on numerical weather predictions [NWP]) is evaluated for one physics-based as well as five different data-driven forecast methods for a year at the same site in south-western Germany. Namely, feed-forward neural networks (FFNN), long short-term memory (LSTM) networks, random forest, bagging and boosting are investigated. Additionally, the forecast quality of the weather forecast is analyzed for key quantities. All evaluated PV forecast methods showed comparable performance; based on concise descriptions of the forecast approaches, advantages and disadvantages of each are discussed. The approaches are viable even though the forecasts regularly differ significantly from the observed behavior; the residual analysis performed offers a qualitative insight into the achievable forecast quality in a typical real-world scenario.

scholarly journals Fisheries in the Mediterranean

2000 ◽  
Vol 1 (1) ◽  
pp. 5 ◽  
Author(s):  
C. PAPACONSTANTINOU ◽  
H. FARRUGIO
Keyword(s):  
Time Series Data ◽  
Early Stage ◽  
Eastern Mediterranean ◽  
Fishing Effort ◽  
Series Data ◽  
Small Scale ◽  
General Description ◽  
Stage Of Development ◽  
Mediterranean Countries ◽  
The Mediterranean

The aim of this paper is to give a description of the Mediterranean fisheries, and its level of exploitation and to address the main questions dealing with its management. The Mediterranean is a semi-enclosed marine area with generally narrow continental shelves. The primary production of the Mediterranean is among the lowest in the world (26-50g C m-2 y-1). The Mediterranean fisheries can be broken down into three main categories: small scale fisheries, trawling and seining fisheries, which operated on demersal, small pelagic and large pelagic resources. After a general description of the state of the resources in the different areas of the Mediterranean it is concluded that (a) the overall pictures from the western to the eastern Mediterranean are not considerably different, (b) the total landings in the Mediterranean have been increased the last decades, and (c) from the perspective of stock assessment, the very few available time series data show stable yield levels. In general fisheries management in the Mediterranean is at a rela- tively early stage of development, judging by the criteria of North Atlantic fisheries. Quota systems are generally not applied, mesh-size regulations usually are set at low levels relative to scientific advice, and effort limitation is not usually applied or, if it is, is not always based on a formal resource assessment. The conservation/management measures applied by the Mediterranean countries can be broadly separated into two major categories: those aiming to keep the fishing effort under control and those aiming to make the exploitation pattern more rational. The most acute problems in the management of the Mediterranean resources are the multispecificity of the catches and the lack of reliable official statistics.

scholarly journals Detecting Regime Transitions of the Nocturnal and Polar Near-Surface Temperature Inversion

2020 ◽  
Vol 77 (8) ◽  
pp. 2921-2940
Author(s):  
Amandine Kaiser ◽  
Davide Faranda ◽  
Sebastian Krumscheid ◽  
Danijel Belušić ◽  
Nikki Vercauteren
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Time Series Data ◽  
Temperature Inversion ◽  
Series Data ◽  
Small Scale ◽  
Dynamical Regime ◽  
Near Surface ◽  
Nonstationary Field ◽  
Regime Transitions

Abstract Many natural systems undergo critical transitions, i.e., sudden shifts from one dynamical regime to another. In the climate system, the atmospheric boundary layer can experience sudden transitions between fully turbulent states and quiescent, quasi-laminar states. Such rapid transitions are observed in polar regions or at night when the atmospheric boundary layer is stably stratified, and they have important consequences in the strength of mixing with the higher levels of the atmosphere. To analyze the stable boundary layer, many approaches rely on the identification of regimes that are commonly denoted as weakly and very stable regimes. Detecting transitions between the regimes is crucial for modeling purposes. In this work a combination of methods from dynamical systems and statistical modeling is applied to study these regime transitions and to develop an early warning signal that can be applied to nonstationary field data. The presented metric aims to detect nearing transitions by statistically quantifying the deviation from the dynamics expected when the system is close to a stable equilibrium. An idealized stochastic model of near-surface inversions is used to evaluate the potential of the metric as an indicator of regime transitions. In this stochastic system, small-scale perturbations can be amplified due to the nonlinearity, resulting in transitions between two possible equilibria of the temperature inversion. The simulations show such noise-induced regime transitions, successfully identified by the indicator. The indicator is further applied to time series data from nocturnal and polar meteorological measurements.

scholarly journals Deep Learning Applied to Scenario Classification for Lane-Keep-Assist Systems

2018 ◽  
Vol 8 (12) ◽  
pp. 2590 ◽  
Author(s):  
Halil Beglerovic ◽  
Thomas Schloemicher ◽  
Steffen Metzner ◽  
Martin Horn
Keyword(s):  
Deep Learning ◽  
Time Series Data ◽  
Heuristic Algorithms ◽  
Measurement Data ◽  
Series Data ◽  
Automated Driving ◽  
Driver Assistance Systems ◽  
Sensor Signals ◽  
Test Verification

Test, verification, and development activities of vehicles with ADAS (Advanced Driver Assistance Systems) and ADF (Automated Driving Functions) generate large amounts of measurement data. To efficiently evaluate and use this data, a generic understanding and classification of the relevant driving scenarios is necessary. Currently, such understanding is obtained by using heuristic algorithms or even by manual inspection of sensor signals. In this paper, we apply deep learning on sensor time series data to automatically extract relevant features for classification of driving scenarios relevant for a Lane-Keep-Assist System. We compare the performance of convolutional and recurrent neural networks and propose two classification models. The first one is an online model for scenario classification during driving. The second one is an offline model for post-processing, providing higher accuracy.

ANALISIS PROSPEK PERKEMBANGAN MODAL DAN TENAGA KERJA PADA INDUSTRI KECIL DI KABUPATEN SIDENRENG RAPPANG

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Agus Syam
Keyword(s):  
Time Series Data ◽  
Small And Medium Enterprises ◽  
Descriptive Analysis ◽  
Series Data ◽  
Small Scale ◽  
Quantitative Descriptive Analysis ◽  
Capital Growth ◽  
Small Industry ◽  
Small Industries ◽  
Small Scale Industries

Abstract Analysis of Prospect Capital Growth and Employment in Small Industries in The District Sidenreng Rappang. This research was conducted to answer the question "what are the prospects of capital growth and labor in the District Small Industries Sidenreng Rappang 5 (five) years from (2012-2016)". Thus, this study aims to determine how the prospect of the development of capital and labor in the District Small Industries Sidenreng Rappang 5 (five) years from (2012-2016). To that end, this study expected to be useful: (1) provide information for Local Government and the Department of Cooperatives and Small and Medium Enterprises in order to foster small-scale industries in the district Sidenreng Rappang, (2) as a reference material other researchers who study small-scale industries in the district Sidenreng Rappang. This research is a descriptive study using data time series (data year) only selected sub-populations in 2007-2011 for the development of capital and labor. Data collection techniques used are; documentation, interviews, and observations. Analysis of the data used, namely: qualitative and quantitative descriptive analysis. Result of research show the; 1) Capital developments in small industry in the District Sidenreng Rappang past five years (2007-2011) has increased by an average of 10.31 percent annually, 2) The development of labor in small-scale industries in the district Sidenreng Rappang past five years (2007-2011) has increased by an average of 1.04 percent annually, and 3) Development prospects of capital and labor in small-scale industries in the district Sidenreng Rappang for the coming five years (2012-2016) amounted to 3.93 percent.Kata Kunci: Modal, Tenaga Kerja dan Industri Kecil

The population dynamics of the bony tongue, Heterotis niloticus (Cuvier, 1829) in the Cross River, Nigeria

2020 ◽  
Vol 29 (1) ◽  
pp. 1-21
Author(s):  
G.A. Otogo ◽  
U.I. Enin
Keyword(s):  
Population Dynamics ◽  
Assessment Tools ◽  
Series Data ◽  
Fishing Mortality ◽  
Population Parameters ◽  
Length Frequency ◽  
K Value ◽  
Cross River ◽  
Exploitation Rate ◽  
The Cross

The population dynamics of Heterotis niloticus of the Cross River, Nigeria was investigated for 15 months (October 2010 – December 2011) with the aim to determine the population parameters including growth and mortality rates. Time series data on the length - frequency distribution was collected from the artisanal landings using cast nets and gill nets at Ayadehe and Oku Ibuko beaches in Itu L.G.A. of Akwa Ibom State. The length - frequency data were analysed using FiSAT (FAO - ICLARM Stock Assessment tools). Maximum length of H. niloticus obtained from the field was 92.0 cm with a corresponding weight of 6.0 kg. Population parameters from length – frequency analysis were estimated as follows: Asymptotic length (L∞) = 103.87cm total length, growth coefficient (K) = 0.32 per year, amplitude of oscillation (C = 0.5) and winter point (WP) = 0.6. The K value of 0.32 showed that the fish is a slow growing species. The WP of 0.6 suggests that H. niloticus experiences slowest growth rate in the months of June – July possibly associated with spawning activity and low ambient temperature recorded during this period. The longevity was estimated at 9 years 4 months and growth performance index (o’) was 3.54. The fish instantaneous total mortality (Z) was estimated at 1.51 per year; natural mortality (M) was 0.608; fishing mortality (F) was 0.902 and exploitation rate (E) of 0.60. This exploitation rate indicates that fishing mortality is becoming excessive and is therefore being overfished. Possible interventions and recommendations are that the effort be reduced or stabilized. The close of fishery in July - August, the peak of reproduction is recommended and should be implemented. In addition, mesh size be increased to reduce growth overfishing on juveniles and sub adults. Key words: Heterotis niloticus, population dynamics, Cross River, Nigeria

scholarly journals Multivariate analysis of nonlinearity in sandbar behavior

2008 ◽  
Vol 15 (1) ◽  
pp. 145-158 ◽  
Author(s):  
L. Pape ◽  
B. G. Ruessink
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Nonlinear Models ◽  
Multivariate Time Series ◽  
Series Data ◽  
Small Scale ◽  
Synthetic Datasets ◽  
Long Term Prediction ◽  
Sandbar Migration

Abstract. Alongshore sandbars are often present in the nearshore zones of storm-dominated micro- to mesotidal coasts. Sandbar migration is the result of a large number of small-scale physical processes that are generated by the incoming waves and the interaction between the wave-generated processes and the morphology. The presence of nonlinearity in a sandbar system is an important factor determining its predictability. However, not all nonlinearities in the underlying system are equally expressed in the time-series of sandbar observations. Detecting the presence of nonlinearity in sandbar data is complicated by the dependence of sandbar migration on the external wave forcings. Here, a method for detecting nonlinearity in multivariate time-series data is introduced that can reveal the nonlinear nature of the dependencies between system state and forcing variables. First, this method is applied to four synthetic datasets to demonstrate its ability to qualify nonlinearity for all possible combinations of linear and nonlinear relations between two variables. Next, the method is applied to three sandbar datasets consisting of daily-observed cross-shore sandbar positions and hydrodynamic forcings, spanning between 5 and 9 years. Our analysis reveals the presence of nonlinearity in the time-series of sandbar and wave data, and the relative importance of nonlinearity for each variable. The relation between the results of each sandbar case and patterns in bar behavior are discussed, together with the effects of noise. The small effect of nonlinearity implies that long-term prediction of sandbar positions based on wave forcings might not require sophisticated nonlinear models.

scholarly journals DINAMIKA POPULASI DAN STATUS PEMANFAATAN UDANG TIGER (Penaeus monodon Fabricius 1798) DI PERAIRAN TARAKAN, KALIMANTAN UTARA

2020 ◽  
Vol 12 (1) ◽  
pp. 11
Author(s):  
Umi Chodrijah ◽  
Ria Faizah ◽  
Tirta Danu
Keyword(s):  
Growth Parameters ◽  
Penaeus Monodon ◽  
Total Mortality ◽  
Optimal Level ◽  
Survey Method ◽  
Von Bertalanffy ◽  
Population Parameters ◽  
Exploitation Rate ◽  
Tiger Prawn ◽  
Exploitation Status

Udang tiger (Penaeus monodon Fabricius 1798) di Tarakan merupakan salah satu komoditas ekspor dan sudah dimanfaatkan cukup lama serta memiliki permintaan dan nilai ekonomis yang tinggi. Penelitian dinamika populasi dan status pemanfaatan udang tiger di perairan Tarakan dan sekitarnya dilakukan untuk mengevaluasi status stok sumberdaya udang agar pengelolaannya dapat berkelanjutan. Penelitian ini dilakukan pada bulan Januari-November 2016 dengan metode survey. Status pemanfaatan diduga berdasarkan laju eksploitasi dan estimasi rasio pemijahan berbasis data panjang (LB-SPR). Hasil pengamatan menunjukkan udang tiger memiliki panjang karapas asimptotik (CL∞) sebesar 65,45 mm, laju pertumbuhan (K) sebesar 1,55 /tahun dan nilai t0 sebesar -0,20/tahun sehingga diperoleh persamaan pertumbuhan Von Bertalanffy CLt = 65,45(1 – e-1,55(t+-0,20)). Laju mortalitas total (Z) sebesar 6,56/ tahun, mortalitas alami (M) sebesar 1,95/tahun, mortalitas penangkapan (F) sebesar 4,62/tahun dan tingkat pemanfaatan (E) sebesar 0,70 /tahun. Tingkat pemanfaatan udang tiger di perairan Tarakan lebih besar dari tingkat pemanfaatan optimal sehingga disarankan untuk menurunkan upaya sebesar 40% dari upaya saat ini.Tiger prawn (Penaeus monodon Fabricius 1798) was one of the export commodity and had been exploited for longtime ago so it was necessary to study about its population parameters and exploitation status for its sustainable management. This research aimed to study about the population parameters and exploitation status of tiger prawn. The research were carried out from January to November 2016 using survey method and the enumeration programme. The growth parameters were based on the Modal Progression Analysis. Exploitation status was estimated based on length based spawning potential ratio (LB-SPR). The results showed that the asymptotic length (CL∞) was 65.45 mm, the growth rate (K) was 1.55 /year and = t0 was -0,20/year so Von Bertalanffy Growth Model was CLt = 65.45(1 - e -1.55(t+-0.20)). Total mortality (Z) was 6.56/years, natural mortality was 1.95/years and fishing mortality was 4.62/years and the highest recruitment of tiger prawns occured in May. The exploitation rate (E) was 0,70/years. The exploitation rate now is higher then the optimal level so it is recommended to reduce 40% of the current efforts.

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