Face Recognition Based on Gabor Feature Extraction Followed by FastICA and LDA

Over the past few decades, face recognition has become the most effective biometric technique in recognizing people’s identity, as it is widely used in many areas of our daily lives. However, it is a challenging technique since facial images vary in rotations, expressions, and illuminations. To minimize the impact of these challenges, exploiting information from various feature extraction methods is recommended since one of the most critical tasks in face recognition system is the extraction of facial features. Therefore, this paper presents a new approach to face recognition based on the fusion of Gabor-based feature extraction, Fast Independent Component Analysis (FastICA), and Linear Discriminant Analysis (LDA). In the presented method, first, face images are transformed to grayscale and resized to have a uniform size. After that, facial features are extracted from the aligned face image using Gabor, FastICA, and LDA methods. Finally, the nearest distance classifier is utilized to recognize the identity of the individuals. Here, the performance of six distance classifiers, namely Euclidean, Cosine, Bray-Curtis, Mahalanobis, Correlation, and Manhattan, are investigated. Experimental results revealed that the presented method attains a higher rank-one recognition rate compared to the recent approaches in the literature on four benchmarked face datasets: ORL, GT, FEI, and Yale. Moreover, it showed that the proposed method not only helps in better extracting the features but also in improving the overall efficiency of the facial recognition system.     

Lighting preferences in office spaces concerning the indoor thermal environment

The accurate prediction of the visual comfort zone in an indoor environment is difficult as it depends on many parameters. This is especially the case for large compact urban areas in which the density and shadow from neighboring buildings can limit the accessible daylighting in indoor spaces. This paper investigates the satisfaction range for illuminance regarding indoor air temperature in office buildings and the significant parameters affecting this range in six office buildings in Tehran, Iran. Lighting comfort has been evaluated by a subjective survey (509 total responses) and field measurement. The questionnaires were filled out in 146 and 109 rooms in summer and winter, respectively. The results show that the illuminance should not be less than 550 lx, while illuminance between 600 and 650 lx provides the highest satisfaction level. The satisfaction with lighting level is affected by individual parameters such as age, type of activity, and environmental parameters such as window orientation, external obscurations, and season. A relationship was observed between lighting level satisfaction and thermal condition acceptance, and the overall comfort depends more on thermal conditions than the lighting level.     

Artificial intelligence and healthcare: Forecasting of medical bookings through multi-source time-series fusion

Nowadays, Artificial intelligence (AI), combined with the digitalization of healthcare, can lead to substantial improvements in Patient Care, Disease Management, Hospital Administration, and supply chain effectiveness. Among predictive analytics tools, time series forecasting represents a central task to support healthcare management in terms of bookings and medical services predictions. In this context, the development of flexible frameworks to provide robust and reliable predictions became a central point in this healthcare innovation process. This paper presents and discusses a multi-source time series fusion and forecasting framework relying on Deep Learning. By combining weather, air-quality and medical bookings time series through a feature compression stage which preserves temporal patterns, the prediction is provided through a flexible ensemble technique based on machine learning models and a hybrid neural network. The proposed system is able to predict the number of bookings related to a specific medical examination for a 7-days horizon period. To assess the proposed approach’s effectiveness, we rely on time series extracted from a real dataset of administrative e-health records provided by the Campania Region health department, in Italy.     

基于多元Taylor级数和AFS的混合定位算法

针对传统Taylor级数定位算法存在精度严重依赖初始值,导致定位精确度不高的缺陷,结合人工鱼群算法和多元Taylor级数展开算法的优点,提出了一种基于人工鱼群算法初值选取与多元Taylor级数展开算法精确求解的混合定位方法。算法充分发挥了人工鱼群算法初值估计性能良好和多元Taylor级数展开算法求解精度高的优点。仿真结果表明:上述算法减少了鱼群数目和迭代次数的选取对定位精度的影响,混合定位算法的精度更高。     

Incomplete analytic hierarchy process with minimum weighted ordinal violations

ABSTRACT

Incomplete pairwise comparison matrices offer a natural way of expressing preferences in decision-making processes. Although ordinal information is crucial, there is a bias in the literature: cardinal models dominate. Ordinal models usually yield nonunique solutions; therefore, an approach blending ordinal and cardinal information is needed. In this work, we consider two cascading problems: first, we compute ordinal preferences, maximizing an index that combines ordinal and cardinal information; then, we obtain a cardinal ranking by enforcing ordinal constraints. Notably, we provide a sufficient condition (that is likely to be satisfied in practical cases) for the first problem to admit a unique solution and we develop a provably polynomial-time algorithm to compute it. The effectiveness of the proposed method is analyzed and compared with respect to other approaches and criteria at the state of the art.     

Artificial pancreas under stable pulsatile MPC: Improving the closed-loop performance

This work presents a pulsatile Zone Model Predictive Control (pZMPC) for the control of blood glucose concentration (BGC) in patients with Type 1 Diabetes Mellitus (T1DM). The main novelties of the algorithm – in contrast to other existing strategies – are: (i) it controls the patient glycemia by injecting short duration insulin boluses for both, the basal and bolus infusions, in an unified manner, (ii) it performs the predictions and estimations (critical to anticipate both, hypo and hyperglycemia) based on a physiological individualized long-term model, (iii) it employs disturbance observers to compensate plant-model mismatches, (iv) it ensures, under standard assumptions, closed-loop stability, and (v) it can be used – under minor modifications – as an optimal basal–bolus calculator to emulate conventional therapies. Because of the latter characteristic, a significantly better performance is achieved, not only in terms of classical indexes (time in the normoglycemia zone, avoidance of hypoglycemia in the short term, avoidance of hyperglycemia in the long term) but also in terms of its applicability (use of the pump or injections). Such a performance is tested in a cohort of in-silico patients from the FDA-accepted UVA/Padova simulation platform, considering the most challenging scenarios.     

A Brief Review of Artificial Intelligence Applications and Algorithms for Psychiatric Disorders

A number of brain research projects have recently been carried out to study the etiology and mechanisms of psychiatric disorders. Although psychiatric disorders are part of the brain sciences, psychiatrists still diagnose them based on subjective experience rather than by gaining insights into the pathophysiology of the diseases. Therefore, it is urgent to have a clear understanding of the etiology and pathogenesis of major psychiatric diseases, which can help in the development of effective treatments and interventions. Artificial intelligence (AI)-based applications are being quickly developed for psychiatric research and diagnosis, but there is no systematic review that summarizes their applications. For this reason, this study briefly reviews three main brain observation techniques used to study psychiatric disorders—namely, magnetic resonance imaging (MRI), electroencephalography (EEG), and kinesics diagnoses—along with related AI applications and algorithms. Finally, we discuss the challenges, opportunities, and future study directions of AI-based applications.     

Robustness analysis and distributed control of a networked system with time-varying delays

This paper is concerned with the robustness analysis and distributed output feedback control of a networked system with uncertain time-varying communication delays. This system consists of a collection of linear time-invariant subsystems that are spatially interconnected via an arbitrary directed network. Using a dissipation inequality that incorporates dynamic hard IQCs (integral quadratic constraints) for the delay uncertainties, we derive some sufficient robustness conditions in the form of coupled linear matrix inequalities, in which the coupled parts reflect the interconnection structure of the system. We then provide a procedure to construct a distributed controller to ensure the robust stability of the closed-loop system and to achieve a prescribed $\ell_2$-gain performance. The effectiveness of the proposed approach is demonstrated by some numerical examples.     

Experimental evaluation of Pt/TiO2/rGO as an efficient HER catalyst via artificial photosynthesis under UVB & visible irradiation

The study investigated the synergistic effects of rGO and Pt over TiO₂ for the HER via artificial photosynthesis under UVB and visible light irradiation. The introduction of glycerol and industrial wastewater to the system as sacrificial reductants signifies that the major reaction pathway is photocatalytic partial water splitting. The material characterizations revealed successful heterojunction formation and provided insight into chemistry behind the activity of the photocatalysts. Amongst various combinations of rGO on TiO₂, 1GNT exhibited an HER yield five times that of bare TiO₂ under UVB light. Addition of Pt led to the formation of a strong Schottky barrier at the heterojunction and consequently boosted HER performance. 1P0.5 GT presented the highest of 28.5 mmol g⁻¹ h⁻¹ with glycerol and 9.6 mmol g⁻¹ h⁻¹ with wastewater under UVB light respectively. For both binary and ternary photocatalysts, the HER performances dwindled under visible light irradiation, accentuating the insufficient activation of the TiO₂. In addition, 1PT outperformed all the other photocatalysts thereby elucidating the impression that rGO and Pt does not work well together in enhancing HER despite quenching the exciton recombination rate of TiO₂ significantly. The role of pH in the synthesis and the experiments has been discussed. Finally, the underlying mechanisms in the photodeposition and photoreformation have been proposed.     

考虑关节角加速度约束的仿人机器人偏摆力矩控制方法

针对仿人机器人步行过程中存在的机器人关节角加速度约束影响控制性能的问题，提出一种考虑关节角加速度约束的仿人机器人偏摆力矩控制方法.该方法充分考虑了双臂在摆动过程中对偏摆力矩的影响，根据力矩平衡条件得到需要抵消的偏摆力矩的大小与方向，将偏摆力矩的控制问题转化为带约束条件的二次规划问题，并设计了一种在线变步长迭代算法计算得到优化后的双臂摆动轨迹.实验表明，该方法能有效抵消机器人步行中产生的偏摆力矩，避免控制过程中的"削峰"现象，有效提高机器人的步行稳定性.