Local Objectives Research Articles

Development of Coastal Tourism to Increase People's Economic Activities (Study on the Community Around Namalatu Beach of Latuhalat Country)

This research is a qualitative descriptive research that aims to find out how to develop coastal tourism to improve the economy of the people around Namalatu Beach, Negeri Latuhalat. This research was carried out at Namalatu Negeri Latuhalat Beach Tourism Object, Ambon City. Primary data collection was carried out through interviews with a number of informants who were directly involved in the Namalatu Beach Tourism Object. The results of this study indicate that the development of coastal tourism to improve the people's economy is not only the responsibility of coastal communities but also good support from the government as a provider of facilities as well as interdependence and cooperation between the government and the community to develop better local tourism objects so that they can improve the economy of the community around the tourist attraction and also increase regional income.

A Violent Nexus: Ethnonationalism, Religious Fundamentalism, and the Taliban

The Taliban is a prime example of a movement that combines a fundamentalist interpretation of religion with a particular form of exclusionary Pashtun ethnonationalism among the jihadist groups. The existing literature on jihadist groups recognizes this trend in so far as a distinction is made between groups that focus on local objectives and others that pursue global objectives such as attacking Western interests. However, there are important gaps in understanding the mechanisms and implications of the interpenetration of religious fundamentalism and ethnonationalism. This article aims to address this gap by unpacking the mechanisms that link religious fundamentalism and ethnonationalism as hybrid ideological underpinnings of the Taliban. It argues that a more nuanced understanding of the entanglement of these ideologies is necessary for developing effective strategies for building peace and inclusive societies.

Improving a model of the hybrid photovoltaic system with a storage battery for local object’s self-consumption involving the setting of power consumed from the grid

The object of research is energy processes in a hybrid photovoltaic system with a storage battery for the needs of a local object involving the setting of power consumed from the network. The task addressed was to build a mathematical model of energy processes with the function of determining control parameters providing for the possibility of changing control scenarios. The mathematical model of the storage battery has been improved, taking into account the charge modes and discharge currents in terms of accuracy of reproduction of the manufacturer’s specification not worse than 3 %. A structure of the model was proposed with separation of module, which defines control parameters, as well as the schedule of power setting for selected scenarios. A variable is introduced into the model description, which determines the specified power value and ensures the construction of SoС(t) schedule. An additional mode to increase the energy use of the photovoltaic battery and restrictions on the measured value of load power were taken into account. Modeling with a change in time scale was proposed: first, control parameters are determined, followed by modeling in the daily cycle. This eliminates the need for preliminary calculations before modeling and provides the ability to verify the determination of system parameters with subsequent adjustment. A procedure for determining control parameters with power setting and adjusting the model under different control scenarios has been devised. When using archival generation data for the location of the facility, this makes it possible at the design stage to choose an option for implementing the power supply system with the desired indicators. For specific uses, it has been shown that underestimating the power of a photovoltaic battery by only 9 % increases energy costs by 1.72–1.39 times. Overstating power by 16.7 % impairs usage by 13.7 % while reducing costs by 1.4 % to 2.5 %

Current Control System for the Peltier Element in a Thermoelectric Cooling Device for Cooling the Human Head

. Thermoelectric semiconductor devices have been widely introduced in various sectors of the national economy. Methods of local thermal exposure are widely used in medical practice in the treatment and prevention of various diseases, such as mechanical injuries and diseases of joints and muscles, pain in the gastrointestinal tract, cholelithiasis and kidney stone disease, neuroses, arthrosis and arthritis, pharyngitis, tonsillitis, chronic fatigue, etc. At the same time, the thermal effect has a significant impact on the energy balance of the body. Under the influence of heat, blood and lymphatic vessels expand, improving blood circulation in many internal organs. The presence of such a device in medicine requires the creation of thermostats capable of influencing a local biophysical object using the Peltier element. This article discusses the development of a mathematical model for human head cooling devices using Peltier elements

Project Portfolio Selection Considering the Fuzzy Chance Constraint of Water Environmental Restoration

The water environment restoration project portfolio (WERP) selection is discussed in this paper. By complying with the analysis of the project’s multidimensional property and operation mode, this paper develops the chance constraint and the management constraint of the WERP from the perspectives of public service and enterprise operation. In addition, the multi-objective mixed integer linear programming model is constructed by combining the expectation method and the fuzzy chance constraint programming method. The results demonstrate that: (1) Our proposed method successfully circumvents the occurrence of local objective optimization within a specific confidence interval, thereby achieving a balance between economic and water environment restoration objectives; (2) including fuzzy chance constraints in our proposed method significantly diminishes the risk of exceeding the WERP capacity, thereby ensuring the effectiveness of water environment restoration by adopting a market-based approach. However, further examination of the impact of various sub-projects in WERP is necessary, along with the integration of novel evolutionary algorithms to enhance the efficiency of our model.

Optimization of spatial arrangement of magnetic field sensors of closed loop system of overhead power lines magnetic field active silencing

Aim. Development of a method for optimization of spatial arrangement and angular position of magnetic field sensors of a closed system to ensure maximum efficiency of active silencing canceling of the magnetic field generated by overhead power lines. Methodology. Spatial arrangement and angular position of magnetic field sensors of closed loop system of overhead power lines magnetic field active silencing determined based on binary preference relations of local objective for multi-objective minimax optimization problem, in whith the vector objective function calculated based on Biot–Savart law. The solution of this vector minimax optimization problem calculated based on nonlinear Archimedes algorithm of multi-swarm multi-agent optimization. Results. Results of simulation and experimental research of optimal spatial arrangement and angular position of magnetic field sensors of a closed system to ensure maximum efficiency of active silencing of the magnetic field generated by overhead power lines with a barrel-type arrangement of wires. Originality. The method for optimization of spatial arrangement and angular position of magnetic field sensors of a closed system to ensure maximum efficiency of active shielding of the magnetic field generated by overhead power lines is developed. Practical value. An important practical problem optimization of spatial arrangement and angular position of magnetic field sensors of a closed system to ensure maximum efficiency of active silencing of the magnetic field generated by overhead power lines has been solved.

FedALA: Adaptive Local Aggregation for Personalized Federated Learning

A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the generalization of the global model on each client. To address this, we propose a method Federated learning with Adaptive Local Aggregation (FedALA) by capturing the desired information in the global model for client models in personalized FL. The key component of FedALA is an Adaptive Local Aggregation (ALA) module, which can adaptively aggregate the downloaded global model and local model towards the local objective on each client to initialize the local model before training in each iteration. To evaluate the effectiveness of FedALA, we conduct extensive experiments with five benchmark datasets in computer vision and natural language processing domains. FedALA outperforms eleven state-of-the-art baselines by up to 3.27% in test accuracy. Furthermore, we also apply ALA module to other federated learning methods and achieve up to 24.19% improvement in test accuracy. Code is available at https://github.com/TsingZ0/FedALA.

Diversified and Realistic 3D Augmentation via Iterative Construction, Random Placement, and HPR Occlusion

In autonomous driving, data augmentation is commonly used for improving 3D object detection. The most basic methods include insertion of copied objects and rotation and scaling of the entire training frame. Numerous variants have been developed as well. The existing methods, however, are considerably limited when compared to the variety of the real world possibilities. In this work, we develop a diversified and realistic augmentation method that can flexibly construct a whole-body object, freely locate and rotate the object, and apply self-occlusion and external-occlusion accordingly. To improve the diversity of the whole-body object construction, we develop an iterative method that stochastically combines multiple objects observed from the real world into a single object. Unlike the existing augmentation methods, the constructed objects can be randomly located and rotated in the training frame because proper occlusions can be reflected to the whole-body objects in the final step. Finally, proper self-occlusion at each local object level and external-occlusion at the global frame level are applied using the Hidden Point Removal (HPR) algorithm that is computationally efficient. HPR is also used for adaptively controlling the point density of each object according to the object's distance from the LiDAR. Experiment results show that the proposed DR.CPO algorithm is data-efficient and model-agnostic without incurring any computational overhead. Also, DR.CPO can improve mAP performance by 2.08% when compared to the best 3D detection result known for KITTI dataset.

Two-Layer Robust Distributed Predictive Control for Load Frequency Control of a Power System under Wind Power Fluctuation

The frequency stability of interconnected power systems becomes quite challenging when incorporating renewable energy sources (mostly wind power). Distributed model predictive control (DMPC) is an effective method to maintain stable grid frequency and realize power system load frequency control (LFC). This paper proposes a two-layer robust DMPC for the LFC of an interconnected power system. In the scheme, the wind power penetrating the power grid is largely affected by the environment condition, and it is taken as an uncertain disturbance to the power system. The two-layer robust DMPC consists of a nominal DMPC controller and an ancillary DMPC controller. The nominal DMPCs coordinate with each other in achieving the systemwide LFC objective, where the systemwide objective is a strict convex combination of the local LFC objectives. The nominal optimization problems are solved supposing the wind power fluctuation is zero. The ancillary DMPC generates the actual control signal for each generation unit based on signals which are transmitted from the nominal DMPC controller. The simulation on a four-area interconnected power system demonstrates the effectiveness of the proposed algorithm in alleviating the frequency deviation caused by varying the load and uncertain wind power fluctuation.

Investigation of spatial variability in power law index of wind profiles above the urban area around Tokyo Bay based on local objective analysis data

By utilizing meteorological local objective analysis (LA) data on a local scale, this study investigated the spatial distribution of the power law index (PLI) of the mean wind speed profile in an urban area around Tokyo Bay in Japan. First, the reliability of the LA data was verified by comparison with observational data. The PLIs of the area for the 16 wind directions at a spatial resolution of 5 km were then estimated using LA data. Under neutral atmospheric conditions, the effects of the roughness length and terrain elevation on the PLI were investigated, and the spatial distribution characteristics of the PLI were analyzed. The results showed that the LA data were consistent with the observational data, indicating that high-precision LA data produced by data assimilation can be a practical supplement to observational data over urban areas. The influence of wind direction on the PLI was evident in the urban area. Furthermore, the metropolis appeared to affect the wind profile significantly when it was within the fetch of other cities, resulting in a larger PLI in the surrounding area. Because the PLI has complex spatial heterogeneity, in addition to urban substrate roughness, changes in terrain elevation and wind direction are thought to affect the PLI significantly. Therefore, generalizing the wind profile characteristics of large-area urban regions using local on-site observational data is not recommended.

Decentralized ADMM with compressed and event-triggered communication

This paper considers the decentralized optimization problem, where agents in a network cooperate to minimize the sum of their local objective functions by communication and local computation. We propose a decentralized second-order communication-efficient algorithm called communication-censored and communication-compressed quadratically approximated alternating direction method of multipliers (ADMM), termed as CC-DQM, by combining event-triggered communication with compressed communication. In CC-DQM, agents are allowed to transmit the compressed message only when the current primal variables have changed greatly compared to its last estimate. Moreover, to relieve the computation cost, the update of Hessian is also scheduled by the trigger condition. Theoretical analysis shows that the proposed algorithm can still maintain an exact linear convergence, despite the existence of compression error and intermittent communication, if the local objective functions are strongly convex and smooth. Finally, numerical experiments demonstrate its satisfactory communication efficiency.

Methodological platform for determining energy efficiency indicators of a complex system

The correct determination of the energy efficiency (EE) of a complex system is a problematic task that requires the use of specialized methods and approaches. Most often, EE is considered as an indicator of the quality-of-service provision, which most fully corresponds to the technical and economic essence of this indicator. The structural and functional organization of the system of such indicators is carried out either on a bottom-up approach or a top-down approach, gradually deploying macroeconomic indicators defined at the system levels. This paper considers the tensor form of organization of the system of such indicators, which allows combining within a single model not only their organizational levels (economy as a whole, economic sectors, enterprises, technologies and equipment), but also the types of activities that reflect these indicators (energy, financial, economic, institutional, environmental, social, etc.), and the factors of changes in energy use (structural, technological, resource, managerial, regulatory, etc.). The paper presents the mapping of this model on the plane of structural and functional factors in the form of classification tables (matrices) of these factors (system elements). The paper describes the procedure for optimizing indicators, which can be carried out for each of these factors separately, their aggregate at each level and for the system as a whole, taking into account the need to implement iterative procedures for coordinating the global (system-wide) goal and local objective functions. Given the use of optimization variables measured in energy and monetary units, this problem is formalized within the framework of the set-theoretic approach. For the purpose of comparing the results, the paper examines the relevant provisions of the International Energy Agency methodology, which uses the direct method of three-factor Laspeyres decomposition analysis, and the Sectoral Methodology for Calculating Harmful Emissions of the Ministry for Communities and Territories Development of Ukraine. Keywords: Energy efficiency, energy efficiency indicators, system analysis, tensor approach.

Distributed convex optimization of higher order nonlinear uncertain multi-agent systems with switched parameters and topologies

This paper investigates the distributed optimization problem for a class of nonlinear uncertain multi-agent systems with unmeasured states, switched parameters, and directed communication topologies changing in the control process. To achieve the goal of optimizing the global objective function, a penalty function is constructed through making up of a sum of local objective functions and integrating consensus conditions of the multi-agent systems to utilize local and neighboring information. Radial basis function neural-networks and neural-networks state observer are applied to approximate the unknown nonlinear functions and obtain the unmeasured states, respectively. To avoid “explosion of complexity” and obtain derivatives for virtual control functions continuously, dynamic surface control technology is proposed to develop a distributed adaptive backstepping neural network control protocol to ensure that all the agents’ outputs asymptotically reach consensus to the optimal solution of the global objective function. Simulations demonstrate the effectiveness of the proposed control scheme.

Distributed Computation of Stochastic GNE With Partial Information: An Augmented Best-Response Approach

In this paper, we focus on the stochastic generalized Nash equilibrium problem (SGNEP) which is an important and widely-used model in many different fields. In this model, subject to certain global resource constraints, a set of self-interested players aim to optimize their local objectives that depend on their own decisions and the decisions of others and are influenced by some random factors. We propose a distributed stochastic generalized Nash equilibrium seeking algorithm in a partial-decision information setting based on the Douglas-Rachford operator splitting scheme, which relaxes assumptions in the existing literature. The proposed algorithm updates players' local decisions through augmented best-response schemes and subsequent projections onto the local feasible sets, which comprise most of the computational workload. The projected stochastic subgradient method is applied to provide approximate solutions to the augmented best-response subproblems for each player. The Robbins-Siegmund theorem is leveraged to establish the main convergence results to a true Nash equilibrium using the proposed inexact solver. Finally, we illustrate the validity of the proposed algorithm via two numerical examples, i.e., a stochastic Nash-Cournot distribution game and a multi-product assembly problem with the two-stage model.

Adversarial Multiagent Output Containment Graphical Game With Local and Global Objectives for UAVs

Multiple leader and follower graphical games constitute challenging problems for aerospace and robotics applications. One of the challenges is to address the mutual interests among the followers with an optimal control point of view. In particular, the traditional approaches treat the output containment problem by introducing selfish followers where each follower only considers their own utility. In this article, we propose a differential output containment game over directed graphs where the mutual interests among the followers are addressed with an objective functional that also considers the neighboring agents. The obtained output containment error system results in a formulation where outputs of all followers prove to converge to the convex hull spanned by the outputs of leaders in a game optimal manner. The output containment problem is solved using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathscr{H}_\infty$</tex-math></inline-formula> output feedback method, where the new necessary and sufficient conditions are presented. Another challenge is to design distributed Nash equilibrium control strategies for such games, which cannot be achieved with the traditional quadratic cost functional formulation. Therefore, a modified cost functional that provides both Nash and distributed control strategies in the sense that each follower uses the state information of its own and neighbors is presented. Furthermore, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathscr{L}_{2}$</tex-math></inline-formula> gain bound of the output containment error system that experiences worst-case disturbances with respect to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathscr{H}_\infty$</tex-math></inline-formula> criterion is investigated. The proposed methods are validated by means of multiagent quadrotor unmanned aerial vehicles output containment game simulations.

Ascent Similarity Caching With Approximate Indexes

Similarity search is a key operation in multimedia retrieval systems and recommender systems, and it will play an important role also for future machine learning and augmented reality applications. When these systems need to serve large objects with tight delay constraints, edge servers close to the end-user can operate as similarity caches to speed up the retrieval. In this paper we present, a new similarity caching policy which improves on the state of the art by using (i) an (approximate) index for the whole catalog to decide which objects to serve locally and which to retrieve from the remote server, and (ii) a mirror ascent algorithm to update the set of local objects with strong guarantees even when the request process does not exhibit any statistical regularity.

Sampled-Data Consensus Protocols for a Class of Second-Order Switched Nonlinear Multiagent Systems.

In this study, the sampled-data consensus problem is investigated for a class of heterogeneous multiagent systems (MASs) in which each agent is described by a second-order switched nonlinear system. Owing to the heterogeneity and the occurrence of dynamic switching in the MASs, the sampled-data consensus protocol design problem is challenging. In this study, two periodic sampled-data consensus protocols and an event-triggered consensus protocol are developed. Here, we first propose a new periodic sampled-data consensus protocol that involves the local objective trajectory interaction among agents. The protocol is then improved by applying the finite-time control and sliding-mode control techniques. Notably, the improved protocol can be implemented without the transmission of constructed auxiliary dynamical variables, which is a major feature of the present study. It is shown that complete consensus of the underlying MASs can be achieved by the two proposed protocols with only sampled-data measurements. To further reduce the communication load, we introduce an event-triggered mechanism to obtain a new protocol. Finally, the effectiveness of the given schemes is demonstrated by considering a numerical example.

Distributed Proximal Gradient Algorithm for Nonconvex Optimization Over Time-Varying Networks

This paper studies the distributed non-convex optimization problem with non-smooth regularization, which has wide applications in decentralized learning, estimation and control. The objective function is the sum of local objective functions, which consist of differentiable (possibly non-convex) cost functions and non-smooth convex functions. This paper presents a distributed proximal gradient algorithm for the non-smooth non-convex optimization problem. Over time-varying multi-agent networks, the proposed algorithm updates local variable estimates with a constant step-size at the cost of multiple consensus steps, where the number of communication rounds increases over time. We prove that the generated local variables achieve consensus and converge to the set of critical points. Finally, we verify the efficiency of the proposed algorithm by numerical simulations.

Unsupervised industrial image ensemble anomaly detection based on object pseudo-anomaly generation and normal image feature combination enhancement

With the development of industrial video technology, the use of cameras rather than a variety of expensive sensors to obtain process or product data has gained more attention. One of the important applications is the use of image data for anomaly detection. It is difficult to collect anomaly data in actual engineering practice, which makes the anomaly detection of industrial products often need to be carried out under the condition of a single data type. How to achieve anomaly detection without anomaly data has become a new challenge. An unsupervised ensemble anomaly detection method based on image enhancement is proposed for image detection with normal data only. The proposed method first uses local pseudo-anomaly generation and object location to generate high-quality pseudo-anomaly images. Then, the pseudo-anomaly images and pseudo-labels are used to guide the training of a reconstruction model and a self-supervised model. In the detection phase, an unsupervised feature screening method is designed to extract sensitive filters, and the normal image features in the feature space output by these sensitive filters are combined and enhanced. Finally, ensemble detection is implemented using different anomaly scores. The experiments show that the proposed method can achieve performance improvements in 15 real datasets.

Herbicide screening for weed control and crop safety in California melon production

AbstractWeed management in cantaloupe and other melon crops is important to maximize fruit yield; however, there are few registered herbicides available in California. Several independent herbicide trials were conducted at University of California field stations in Davis (Yolo County), Five Points (Fresno County), and Holtville (Imperial County) from 2013 to 2019 to evaluate both registered and unregistered herbicides and incorporation methods (sprinklers, cultivation, or none) for crop safety and weed control in melons. Although specific treatments varied among locations depending on local practice and research objectives, ethalfluralin and halosulfuron were used in all experiments, and bensulide and S-metolachlor were evaluated in 4 of 6 site-years. Additional herbicides included clethodim, clomazone, DCPA, napropamide, pendimethalin, sethoxydim, and sulfentrazone. Among registered herbicides, halosulfuron, halosulfuron + ethalfluralin, and ethalfluralin + bensulide combinations provided consistently beneficial weed control across all site-years compared to the nontreated control. S-metolachlor performed as well as the best of the registered herbicides tested at each site-year; although moderate injury was noted at the Davis location, this did not reduce melon yield. The method used to incorporate preplant herbicides had a significant impact on weed control efficacy but varied by location. Mechanical incorporation of preplant herbicides resulted in improved weed control and yield compared to sprinklers. Early-season weed control, whether by herbicides or hand weeding, resulted in significant yield increase in most site-years.