Remote sensing is one of the most important methods for analysing the multitemporal changes over a certain period. As a cost-effective way, remote sensing allows the long-term analysis of agricultural land by collecti...
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Remote sensing is one of the most important methods for analysing the multitemporal changes over a certain period. As a cost-effective way, remote sensing allows the long-term analysis of agricultural land by collecting satellite imagery from different satellite missions. Landsat is one of the longest-running world missions which offers a moderate-resolution earth observation dataset. Land surface mapping and monitoring are generally performed by incorporating classification and change detection models. In this work, a deep learning-based change detection (DCD)a* algorithm has been proposed to detect long-term agricultural changes using the Landsat series datasets (i.e., Landsat-7, Landsat-8, and Landsat-9) during the period 2012 to 2023. The proposeda* algorithm extracts the features from satellite data according to their spectral and geographic characteristics and identifies seasonal variability. The DCD integrates the deep learning-based (Environment for visualizing images) ENVI Net-5 classification model and posterior probability-based post-classification comparison-based change detection model (PCD). The DCD is capable of providing seasonal variations accurately with distinct Landsat series dataset and promises to use higher resolution dataset with accurate results. The experimental result concludes that vegetation has decreased from 2012 to 2023, while build-up land has increased up to 88.22% (2012-2023) for Landsat-7 and Landsat-8 datasets. On the other side, degraded area includes water (3.20-0.05%) and fallow land (1-0.59%). This study allows the identification of crop growth, crop yield prediction, precision farming, and crop mapping.
With the explosion of innovation driven by generative and traditional artificial intelligence (AI), comes the necessity to understand and regulate products that often defy current regulatory classification. Tradition,...
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With the explosion of innovation driven by generative and traditional artificial intelligence (AI), comes the necessity to understand and regulate products that often defy current regulatory classification. Tradition, and lack of regulatory expediency, imposes the notion of force-fitting novel innovations into pre-existing product classifications or into the essentially unregulated domains of wellness or consumer electronics. Further, regulatory requirements, levels of risk tolerance, and capabilities vary greatly across the spectrum of technology innovators. For example, currently unregulated information and consumer electronic suppliers set their own editorial and communication standards without extensive federal regulation. However, industries like biopharma companies are held to a higher standard in the same space, given current direct-to-consumer regulations like the Sunshine Act (also known as Open Payments), the federal Anti-Kickback Statute, the federal False Claims Act, and others. Clear and well-defined regulations not only reduce ambiguity but facilitate scale, showcasing the importance of regulatory clarity in fostering innovation and growth. To avoid highly regulated industries like health care and biopharma from being discouraged from developing AI to improve patient care, there is a need for a specialized framework to establish regulatory evidence for AI-based medical solutions. In this paper, we review the current regulatory environment considering current innovations but also pre-existing legal and regulatory responsibilities of the biopharma industry and propose a novel, hybridized approach for the assessment of novel AI-based patient solutions. Further, we will elaborate the proposed concepts via case studies. This paper explores the challenges posed by the current regulatory environment, emphasizing the need for a specialized framework for AI medical devices. By reviewing existing regulations and proposing a hybridized approach, we aim to ensure
Satellite remote sensing is the best way to derive sea surface skin temperature (SSTskin) in the Arctic. However, as surface temperature retrievala* algorithms in the infrared (IR) part of the electromagnetic spectrum a...
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Satellite remote sensing is the best way to derive sea surface skin temperature (SSTskin) in the Arctic. However, as surface temperature retrievala* algorithms in the infrared (IR) part of the electromagnetic spectrum are designed to compensate for atmospheric effects mainly due to water vapor, MODIS SSTskin retrievals have larger uncertainties at high latitudes where the atmosphere is very dry and cold, which is an extreme in the distribution of global conditions. MODIS R2019 SSTskin fields are currently derived using latitudinally and monthly dependenta* algorithm coefficients, including an additional band above 60 degrees N to better represent the effects of Arctic atmospheres. However, the R2019 processing of MODIS SSTskin still has some unrevealed error characteristics. This study uses 21 years (2002-2022) of collocated, simultaneous satellite brightness temperature (BT) data from Aqua MODIS and in situ buoy-measured subsurface temperature data from iQuam for validation. Unlike elsewhere over the oceans, the 11 mu m and 12 mu m BT differences are poorly related to the column water vapor at high latitudes, resulting in poor atmospheric water vapor correction. Anomalous BT difference signals are identified, caused by the temperature and humidity inversions in the lower troposphere, which are especially significant during the summer. Although the existence of negative BT differences is physically reasonable, this makes the retrievala* algorithm lose its effectiveness. Moreover, the statistics of the MODIS SSTskin data when compared with the iQuam buoy temperature data show large differences (in terms of mean and standard deviation) for the matchups at the Northern Atlantic and Pacific sides of the Arctic due to the disparity of in situ measurements and distinct surface and vertical atmospheric conditions. Therefore, it is necessary to further improve the retrievala* algorithms to obtain more accurate MODIS SSTskin data to study surface ocean processes and climate change i
This paper proposes a hybrid analog-digital architecture, aimed at reducing hardware resource consumption and improving the scalability of a multi -channel feedforward active noise control system to accommodate a larg...
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This paper proposes a hybrid analog-digital architecture, aimed at reducing hardware resource consumption and improving the scalability of a multi -channel feedforward active noise control system to accommodate a large number of error microphones. This hybrid architecture employs a partial -update filtered -x least mean squaresa* algorithm that is lightweight and suitable for real-time execution, as it updates the control filters in each iteration using only two selected error signals. The selection of these two error signals is achieved through a dedicated analog circuit comprising analog comparators and multiplexers. Thus, the hybrid architecture requires only two analog -to -digital converters for error signals, regardless of the number of error microphones. Experiments were conducted on two active noise control casings, demonstrating that, with a specific computational capacity, the proposed hybrid architecture can accommodate significantly longer control filters, resulting in higher steady-state noise reduction. Moreover, additional error microphones were introduced to demonstrate the scalability. The hybrid architecture simplifies the implementation of the multi -channel feedforward active noise control system when additional error microphones are needed for more evenly distributed residual noise levels or a larger zone of quiet.
We study the randomized n-th minimal errors (and hence the complexity) of vector valued mean computation, which is the discrete version of parametric integration. The results of the present paper form the basis for th...
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We study the randomized n-th minimal errors (and hence the complexity) of vector valued mean computation, which is the discrete version of parametric integration. The results of the present paper form the basis for the complexity analysis of parametric integration in Sobolev spaces, which will be presented in Part 2. Altogether this extends previous results of Heinrich and Sindambiwe (1999) [12] and Wiegand (2006) [27]. Moreover, a basic problem of Information-Based Complexity on the power of adaption for linear problems in the randomized setting is solved. (c) 2023 Elsevier Inc. All rights reserved.
Nowadays, screens are very common in our daily life. There are several di erent kinds of screens, LCD, LED, OLED, ULED and so on. LCD screens can display high-resolution pictures while LED has advantages of low energy...
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Nowadays, screens are very common in our daily life. There are several di erent kinds of screens, LCD, LED, OLED, ULED and so on. LCD screens can display high-resolution pictures while LED has advantages of low energy consumption and wider color range. This project has two goals. The rst one is to achieve a seamless display screen which consists of 9 LED backlit LCD boards. The second goal is to improve image quality, which is enabled by the combination of LED and LCD. There are two main problems that need to be solved in this project. The rst problem is brightness correction. Because of the projection method and the distance between lights and nal screen, there are di erent kinds of overlapping situations and distinct lines on screen. The other one is the combination of LED and LCD. Thea* algorithms need to be developed to ensure that RGB LEDs and LCD panels display the same picture and to address some problems caused by the LCD module.
We present a robust battery energy storage system (BESS) management strategy for simultaneous participation in frequency containment reserve (FCR) and automatic frequency restoration reserve (aFRR) provision with mark...
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We present a robust battery energy storage system (BESS) management strategy for simultaneous participation in frequency containment reserve (FCR) and automatic frequency restoration reserve (aFRR) provision with market-based state of charge (SOC) restoration exclusively via intraday market. The study is motivated by the developments and harmonisation of the regulatory framework of European balancing markets and the rapidly increasing role of BESS in power system regulation. The proposed strategy involves BESS SOC restoration through scheduled transactions in the intraday market. It also includes reserve mode and recovery status management benefiting from regulatory alleviations granted to FCR providers that are qualified as limited energy reservoirs, and voluntary aFRR energy bid preparation process. Altogether, the management strategy is based on a worst-case activation anticipation approach, meaning that non-delivery of contracted reserves is under no circumstances permitted. Moreover, activation overfulfilment and FCR deadband utilisation are not allowed for battery charge restoration in line with the recent regulation. The strategy is particularly useful for balancing reserve providers that do not have options to restore the BESS energy content within their own portfolio, thus having to rely on wholesale markets, which can have significant lead time before trade delivery. Based on simulations, we first validate the robustness of our strategy in an extreme worst-case scenario and then provide case studies utilising power system operational data from Germany and Finland, showcasing the technical and economic performance of the devised strategy under realistic and diverse conditions. While our approach builds on the upcoming Baltic balancing market framework, due to the ongoing European balancing market harmonisation, it is applicable also to the EU markets in general.
The combination of lifelong learninga* algorithms with autonomous intelligent systems (AIS) is gaining popularity due to its ability to enhance AIS performance, but the existing summaries in related fields are insuffici...
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The combination of lifelong learninga* algorithms with autonomous intelligent systems (AIS) is gaining popularity due to its ability to enhance AIS performance, but the existing summaries in related fields are insufficient. Therefore, it is necessary to systematically analyze the research on lifelong learninga* algorithms with autonomous intelligent systems, aiming to gain a better understanding of the current progress in this field. This paper presents a thorough review and analysis of the relevant work on the integration of lifelong learninga* algorithms and autonomous intelligent systems. Specifically, we investigate the diverse applications of lifelong learninga* algorithms in AIS's domains such as autonomous driving, anomaly detection, robots, and emergency management, while assessing their impact on enhancing AIS performance and reliability. The challenging problems encountered in lifelong learning for AIS are summarized based on a profound understanding in literature review. The advanced and innovative development of lifelong learninga* algorithms for autonomous intelligent systems are discussed for offering valuable insights and guidance to researchers in this rapidly evolving field.
The paper devoted to the boundary value problem with parameter for second order system of hyperbolic equations. We study of a questions for existence and uniqueness of solution to the problem and a construction of alg...
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The paper devoted to the boundary value problem with parameter for second order system of hyperbolic equations. We study of a questions for existence and uniqueness of solution to the problem and a construction ofa* algorithms for finding its solution. Conditions for the unique solvability to problem with parameter are established in the terms of fundamental matrix and initial data.
A directed graph where there is exactly one edge between every pair of vertices is called a tournament. Finding the "best" set of vertices of a tournament is a well -studied problem in social choice theory. ...
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A directed graph where there is exactly one edge between every pair of vertices is called a tournament. Finding the "best" set of vertices of a tournament is a well -studied problem in social choice theory. A tournament solution takes a tournament as input and outputs a subset of vertices of the input tournament. However, in many applications, for example, choosing the best set of drugs from a given set of drugs, the edges of the tournament are given only implicitly and knowing the orientation of an edge is costly. In such scenarios, we would like to know the best set of vertices (according to some tournament solution) by "querying" as few edges as possible. We, in this paper, precisely study this problem for commonly used tournament solutions: given an oracle access to the edges of a tournament T, find(T) by querying as few edges as possible, for a tournament solution. We first study some popular tournament solutions and show that any deterministica* algorithm for finding the Copeland set, the Slater set, the Markov set, the bipartisan set, the uncovered set, the Banks set, and the top cycle must query omega(2) edges in the worst case. We also show similar lower bounds on the expected query complexity of these tournament solutions by any randomizeda* algorithm. On the positive side, we are able to circumvent our strong query complexity lower bound results by proving that, if the size of the top cycle of the input tournament is at most, then we can find all the tournament solutions mentioned above by querying edges only.
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