Muhammad Tariq Siddique, Ibrahim Venkat, Shah Hasan Shah Newaz
et al.
The challenges imposed by the Single Sample Per Person face recognition problem become especially critical when face images are captured in uncontrolled environments, which typically involve variations in illumination, facial expression, pose, occlusion, and more. In particular, illumination changes caused by poor lighting conditions can significantly degrade the performance of face recognition systems. In this paper, we investigate the SSPP face recognition problem in the presence of potential illumination variation and propose a two-fold approach. Firstly, we analyse and quantify the illumination variations in face images and then normalise these variations based on a probabilistic image enhancement approach. Subsequently, the feature embedding process was performed using the deep learning-based FaceNet system, and finally, faces were classified using the classical Support Vector Machine. The performance of the proposed approach is demonstrated through comprehensive experiments compared to state-of-the-art techniques. The proposed method achieved significant accuracy improvements, attaining 96.17% and 95.68% for 20 and 30 subjects, respectively, on the Extended Yale-B dataset. The performance of the proposed method is evaluated and exhibits superiority in handling illumination compared to state-of-the-art counterparts.
Low-elevation mountainous (LEM) areas in some countries are reclaimed for agriculture (especially, for deep cultivation of tea trees) due to their accessibility, higher temperature, and more rainfall, contributing to biodiversity and ecological balance. However, extensive development in these areas causes lasting ecological damage; and hence it is crucial to evaluate the ecological value of these areas for formulating relevant ecological protection policies. Therefore, this study evaluated the ecosystem service value of tea plantations in LEM areas using the contingent valuation method based on the subjective consciousness of farmers, rather than an objective assessment. A questionnaire survey was conducted on tea farmers in Mingjian Township (which is one of the major tea-producing LEM areas in Mid-Taiwan); and ecosystem service values of tea plantations between conventional farming methods (CFMs) and organic farming methods (OFMs) were compared. Firstly, the differences in willingness to pay (WTP) for the samples including and excluding protest responses were investigated, and then a regression analysis to estimate the WTP price for maintaining the tea plantation ecology was conducted. Empirical results showed a significantly negative correlation between the WTP price and the education level of farmers in tea plantations using CFMs, but a significantly positive correlation in tea plantations using both CFMs and OFMs. Most respondents were male and used CFMs to operate tea plantations; and those with higher monthly income and using both CFMs and OFMs were willing to pay more. Regarding ecosystem services of tea plantations, the respondents preferred the “provisioning” ecosystem services, and were willing to pay an average of NT$ 718.92 and NT$ 794.32 per year for maintaining the ecology of tea plantations using CFMs and OFMs, respectively. The unit ecological value of tea plantations was not inferior to those of coniferous and broad-leaved forests, paddy fields, and algae reefs from previous studies. Finally, some suggestions are proposed for a reference in formulating relevant ecological protection policies.
Abstract Beluga Whale Optimization (BWO) is a new metaheuristic algorithm that simulates the social behaviors of beluga whales swimming, foraging, and whale falling. Compared with other optimization algorithms, BWO shows certain advantages in solving unimodal and multimodal optimization problems. However, the convergence speed and optimization performance of BWO still have some performance deficiencies when solving complex multidimensional problems. Therefore, this paper proposes a hybrid BWO method called HBWO combining Quasi-oppositional based learning (QOBL), adaptive and spiral predation strategy, and Nelder-Mead simplex search method (NM). Firstly, in the initialization phase, the QOBL strategy is introduced. This strategy reconstructs the initial spatial position of the population by pairwise comparisons to obtain a more prosperous and higher quality initial population. Subsequently, an adaptive and spiral predation strategy is designed in the exploration and exploitation phases. The strategy first learns the optimal individual positions in some dimensions through adaptive learning to avoid the loss of local optimality. At the same time, a spiral movement method motivated by a cosine factor is introduced to maintain some balance between exploration and exploitation. Finally, the NM simplex search method is added. It corrects individual positions through multiple scaling methods to improve the optimal search speed more accurately and efficiently. The performance of HBWO is verified utilizing the CEC2017 and CEC2019 test functions. Meanwhile, the superiority of HBWO is verified by utilizing six engineering design examples. The experimental results show that HBWO has higher feasibility and effectiveness in solving practical problems than BWO and other optimization methods.
Computer engineering. Computer hardware, Information technology
Abstract With the rapid development and deep application of artificial intelligence, modern air combat is incrementally evolving towards intelligent combat. Although deep reinforcement learning algorithms have contributed to dramatic advances in in air combat, they still face challenges such as poor interpretability and weak transferability of adversarial strategies. In this regard, this paper proposes a tactical intent-driven method for autonomous air combat behaviour generation. Firstly, this paper explores the mapping relationship between optimal strategies and rewards, demonstrating the detrimental effects of the combination of sparse rewards and dense rewards on policy. Built around this, the decision-making process of pilot behavior is analyzed, and a reward mapping model from intent to behavior is established. Finally, to address the problems of poor stability and slow convergence speed of deep reinforcement learning algorithms in large-scale state-action spaces, the dueling-noisy-multi-step DQN algorithm is devised, which not only improves the accuracy of value function approximation but also enhances the efficiency of space exploration and network generalization. Through experiments, the conflicts between sparse rewards and dense rewards are demonstrated. The superior performance and stability of the proposed algorithm compared to other algorithms are captured by our empirical results. More intuitively, the strategies under different intents exhibit strong interpretability and flexibility, which can provide tactical support for intelligent decision-making in air combat.
Electronic computers. Computer science, Information technology
This study introduces a novel experience-mapping methodology designed to alleviate the challenge of delayed comprehension in education. Education often entails a delayed understanding of its content and value. This comprehension lag often results in discrepancies between learners and educational content, potentially leading to setbacks in the learning process. In response, we present a mapping model that delineates the essential structure of educational content and positioning between the learner and the content. This model serves as a guiding roadmap, enabling learners to navigate the complexities of educational content through a pair of constructed semantic networks. These networks reflect insights from recent brain science and educational experience studies. This study delves into the application of the STEM (Science, Technology, Engineering, Mathematics) education. Furthermore, we discuss the potential of experience mapping within the spheres of curriculum design and faculty development. Through these applications, this research contributes to the development of educational.
Johannes C. Joubert, Daniel N. Wilke, Patrick Pizette
This paper presents a GPU-based, incompressible, multiphase generalized finite difference solver for simulating multiphase flow. The method includes a dampening scheme that allows for large density ratio cases to be simulated. Two verification studies are performed by simulating the relaxation of a square droplet surrounded by a light fluid and a bubble rising in a denser fluid. The scheme is also used to simulate the collision of binary droplets at moderate Reynolds numbers (250–550). The effects of the surface tension and density ratio are explored in this work by considering cases with Weber numbers of 8 and 180 and density ratios of 2:1 and 1000:1. The robustness of the multiphase scheme is highlighted when resolving thin fluid structures arising in both high and low density ratio cases at We = 180.
Cheryl Forchuk, Jonathan Serrato, Daniel Lizotte
et al.
Smart home technologies present an unprecedented opportunity to improve health and health care by providing greater communication and connectivity with services and care providers and by supporting the daily activities of people managing both mental and physical health problems. Based on our experience from conducting smart technology health studies, including a smart home intervention, we provide guidance on developing and implementing such interventions. First, we describe the need for an overarching principle of security and privacy that must be attended to in all aspects of such a project. We then describe 4 key steps in developing a successful smart home innovation for people with mental and physical health conditions. These include (1) setting up the digital infrastructure, (2) ensuring the components of the system communicate, (3) ensuring that the system is designed for the intended population, and (4) engaging stakeholders. Recommendations on how to approach each of these steps are provided along with suggested literature that addresses additional considerations, guidelines, and equipment selection in more depth.
Information technology, Public aspects of medicine
Besides many advantages, the reduction in the operational duty of a traditional phase shifted full converter limits its scope in applications where a wide range of input voltage is the main requirement. Operation with low duty cycle extends freewheeling interval, which results in degraded performance such as more circulating current, increased conduction loss in power devices, narrow range of zero voltage switching and increased EMI. To overcome these drawbacks, this work suggests a modified phase shifted full bridge converter that keeps the operational duty of the converter high for a wide range of input voltage. This cuts the freewheeling interval and improves performance. The proposed converter consists of four low profile transformers having a structure of reconfigurable interconnections. There are two distinct reconfigurable operational modes, a low-gain mode and a high-gain mode, which can be adopted following the variation in line voltage. The proposed work is validated in LTspice simulation and hardware characterization for a wide range of input voltage 100-400 Vdc/ 12 Vout and up to the load power of 1.2 kW.
This review analyses the influence of technologies and saving propensities of workers and shareholders on economic growth, considering the [1] model. We show how investing behaviors and production peculiarities condition the evolution of capital over time. We highlight that fluctuations and multiple equilibria arise only when the elasticity of substitution between capital and labor is lower than one. Moreover, only production functions with variable elasticity of substitution between inputs are able to describe the poverty trap phenomenon. Complex dynamics emerge when the difference between the saving propensity of the two income groups is sufficiently high.
Ega Silvana Almunadia, Tien Fabrianti Kusumasari, Iqbal Santosa
Perum Perhutani is a State-Owned Enterprise (SOE) that focus on forest management. One of the Perum Perhutani’s main businesses is Agroforestry. To achieve the business goals of Agroforestry, Perum Perhutani requires an information system that is capable of supporting its management activities. In developing IT, Perum Perhutani has a reference, that is the SOE Regulation Number: PER-03 / MBU / 02/2018 regarding the Information Technology Management Preparation Guide that every BUMN must align business strategy with the IT strategy. So, Perum Perhutani requires the design of Enterprise Architecture. The framework that will be used in designing Enterprise Architecture is the Open Group Architecture Framework (TOGAF) and the TOGAF Architecture Development Method (ADM) method. The output of this design is a blueprint and IT Roadmap for 5 years that can be used as an implementation guide.
The world of ergonomic evaluation considerate the human biomechanics and anthropometric measurement an integral part of product design and development work. In this paper, we have given an attempt to design an ergonomically fitted office chair suitable for Bangladeshi people. In this paper, the anthropometric data analysis has been done in order to determine the necessary dimensions suitable for the office chair. Lastly, an ergonomically fitted office chair is designed based on this anthropometric data analysis. The concept of the paper is to focus on the dimensional changes that the Bangladeshi people need for their comfort in the ergonomic office chair. The structural difference in different regions makes us inspired to think about the office chair ergonomics for Bangladeshi people. In short, this paper reflects the entire process of designing an ergonomic office chair suitable for them.
Manochehr Manteghi, Alireza Booshehri, Gholam Reza Tavakoli
et al.
Nowadays due to taking place rapid changes, successful organizations are looking for the vision, goals and strategic objectives of their business as this requires entails applying innovation in organizations to meet current and future demands of the stakeholders. In this regard climate of innovation is one of the most important organizational capabilities that understanding and reinforcement it, leads to reduction in time and cost of transforming into an innovative organization. So the present study was carried out in the territory of Acrospace Industries Organization, taking into account the problem that "Prolonging and increasing the cost of applying innovation to produce innovative products with reasonable price and desirable quality", seeks to answer to these questions: What is the quantative rate of the climate of innovation in this organization? And what are the areas for improvemet of innovation climate in a defense organization with innovative CoPS? The strategy of research was survey, using evaluation method based on the climate of innovation questionnaire and purposive sampling by focusing on 44 executives and senior experts in "Planning" and "Research and Innovation" units of organization and its subsidiaries staff. The analysis of the data showed that the amount of innovative climate was 59.9% and explained 24 improvement areas that most of them are related to "risk-taking, admit of errors and failures" and "support for innovation" and "trust, transparency, honesty".
The cloud-computing concept has emerged as a powerful mechanism for data storage by providing a suitable platform for data centers. Recent studies show that the energy consumption of cloud computing systems is a key issue. Therefore, we should reduce the energy consumption to satisfy performance requirements, minimize power consumption, and maximize resource utilization. This paper introduces a novel algorithm that could allocate resources in a cloud-computing environment based on an energy optimization method called Sharing with Live Migration (SLM). In this scheduler, we used the Cloud-Sim toolkit to manage the usage of virtual machines (VMs) based on a novel algorithm that learns and predicts the similarity between the tasks, and then allocates each of them to a suitable VM. On the other hand, SLM satisfies the Quality of Services (QoS) constraints of the hosted applications by adopting a migration process. The experimental results show that the algorithm exhibits better performance, while saving power and minimizing the processing time. Therefore, the SLM algorithm demonstrates improved virtual machine efficiency and resource utilization compared to an adapted state-of-the-art algorithm for a similar problem.
Traditional Aceh cuisine is a dish cooked by the people of Aceh and marinade formulated by the people of Aceh. Based on the results of interviews with the community around the traditional dishes are often cooked every day such as "kuah plik/ patarana, kuah asam keu-eung, kuah masak puteh, kuah lemak/santan, kuah masak mirah, boh manok U, asam sabee, bileh payeh, asam u, dan urap Aceh". Nowadays according to the advanced application can help the young mothers who from not able to cook become skilful cooking Aceh cuisine, one of them is from the application of traditional recipe of Aceh-based recipe android so that the young mothers can be assisted in the process of way cooking Aceh cuisine. In implementing the AHP method, the authors use the method on the portion of the cuisine divided into 3 (three) portion division of which the three portion, the seven portions and the 12 portions. We show that results in rankings of the most desirable of the 12th share with the highest ranking value of the calculation are 0.37.