A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images

In line with the development of Industry 4.0, surface defect detection/anomaly detection becomes a topical subject in the industry field. Improving efficiency as well as saving labor costs has steadily become a matter of great concern in practice, where deep learning-based algorithms perform better than traditional vision inspection methods in recent years. While existing deep learning-based algorithms are biased towards supervised learning, which not only necessitates a huge amount of labeled data and human labor, but also brings about inefficiency and limitations. In contrast, recent research shows that unsupervised learning has great potential in tackling the above disadvantages for visual industrial anomaly detection. In this survey, we summarize current challenges and provide a thorough overview of recently proposed unsupervised algorithms for visual industrial anomaly detection covering five categories, whose innovation points and frameworks are described in detail. Meanwhile, publicly available datasets for industrial anomaly detection are introduced. By comparing different classes of methods, the advantages and disadvantages of anomaly detection algorithms are summarized. Based on the current research framework, we point out the core issue that remains to be resolved and provide further improvement directions. Meanwhile, based on the latest technological trends, we offer insights into future research directions. It is expected to assist both the research community and industry in developing a broader and cross-domain perspective

On the dynamic behavior and stability of controlled connected Rayleigh beams under pointwise output feedback

We study the dynamic behavior and stability of two connected Rayleigh beams that are subject to, in addition to two sensors and two actuators applied at the joint point, one of the actuators also specially distributed along the beams. We show that with the distributed control employed, there is a set of generalized eigenfunctions of the closed-loop system, which forms a Riesz basis with parenthesis for the state space. Then both the spectrum-determined growth condition and exponential stability are concluded for the system. Moreover, we show that the exponential stability is independent of the location of the joint. The range of the feedback gains that guarantee the system to be exponentially stable is identified

Nanoparticles and alloys for therapeutical and structural biomedical applications.

This thesis addresses 2 challenges in biomaterials research: 1) diffusion phenomena in Ti-Al-Nb alloys as materials for structural applications; and 2) the development of magnetic hyperthermia therapies against cancer more efficient and less invasive. Both challenges share a characteristic physical ground, which is the guideline of this work: they are based on transfer phenomena, mass transfer in the first case, and heat transfer in the second.Biomaterials research has been in an ascendant trend over the last decades. In biomedical applications, the first thing to be taken into consideration is biocompatibility. This property together with high specific strength, and good corrosion resistance has made titanium and its alloys the preferred materials for structural applications in the human body. Moreover, they have also been widely used in other fields like aerospace and marine industries. The composition of alloys is the most basic parameter that determines their properties. For instance, compared with the conventional Ti-6Al-4V alloy, some vanadium free titanium alloys like Ti-Al-Nb alloys, have higher fatigue strength, lower modulus of elasticity, and improved biocompatibility. All these properties are closely related to their microstructures that can be engineered by recovery, recrystallization, grain growth, transformation and precipitation. Furthermore, microstructural features can also be controlled to some extent by diffusion phenomena.Bibliometric studies show that in the uprising of Biomaterials research "Nanoparticles" has become the hottest topic after the turn of millennium. Indeed, nanotechnology, having been at the forefront of research for many years, has brought new genuine technical solutions in many different fields like biology, materials, electronics and medicine etc. One of the most exciting among them is that of therapeutical applications of nanoparticles (NPs), in which toxicity is also the main concern. For instance, in NP mediated magnetic hyperthermia for cancer therapy, only iron oxide nanoparticles (IONP), and particularly maghemite (-Fe2O3), are clinically accepted, in spite of existence of other materials like Co ferrite (CoFe2O4) that present clear advantages in terms of heating performance but show toxicity issues. Therefore, research efforts in this area have been mostly devoted to improve the performance of maghemite NPs by optimizing their structural parameters such as size, size distribution, shape, crystallinity, etc. There is however another polymorph of iron oxide, -Fe2O3, that has exceptional magnetic properties, but nevertheless has never been explored as a potential candidate for magnetic hyperthermia therapy.The idea of hyperthermia is to elevate the temperature of the tumor tissue over 42 ℃, in a selective way, to cause the apoptotic death of cancer cells. In order to heat selectively the tumor, it is peremptory to precisely monitor and control the temperature of the surrounding healthy tissue. Moreover, actual clinical magnetic hyperthermia technology uses massive direct injection of nanoparticles, which carries out some degree of invasiveness and toxicity issues. In order to avoid these problems and to expand the use of this technology in clinics, a new strategy has emerged that requires a reduced heat production. It is based on applying small amounts of heat but concentrated at certain intracellular regions that may lead to cancer cell apoptosis. To proof this hypothesis, it is first necessary to determine whether the heat produced by the MNPs is enough to generate large temperature gradients in small intracellular regions in the competition with heat dissipation process across the cell cytoplasm and then to the extracellular matrix. For this purpose, a non-invasive thermometric technique is required capable to determine local temperatures inside the cells with ultra-high spatial resolution. In this matter the use of lanthanide-based luminescent molecular thermometers can be a good option, as it will be shown in this thesis.This thesis is about: the diffusion phenomenon in the Ti-Al-Nb alloys, the hyperthermia performance of epsilon iron oxide nanoparticles, the fine-tuning of a ultra-high spatial and time resolution 2D temperature imaging system, the performance of Ln3+-bearing nanoparticles as nano-thermometry probes, obtaining intracellular temperature images, and the determination of temperature gradients in magnetic nanoparticles inside cancer cells under an ac magnetic field irradiation, and finally to investigate the validity of the local hyperthermia hypothesis.Chapter 1 will give a general introduction to the application of Titanium alloys and magnetic nanoparticles. The focus concerning titanium alloys will be put on diffusion phenomena, while in the case of magnetic nanoparticles, it will be mainly directed to magnetic hyperthermia and molecular nanothermometry.Chapter 2 contains the experimental section including methods, preparation and characterization of Titanium alloys, and magnetic and thermometric nanoparticle suspensions, and a description of the temperature imaging system.Chapter 3 is focused on diffusion phenomenon study of body centered cubic Ti-Al-Nb alloys by both experimental and computational methods, and the construction of a diffusion kinetic database. The experiments were conducted by the diffusion couple technique, and the computational work thereafter was accomplished by the DICTRA software.Chapter 4 and 5 demonstrates the hyperthermia performance of pure and Ga-doped epsilon iron oxide nanoparticles, in comparison with that of gamma iron oxide nanoparticles.Chapter 6 is dedicated to intracellular 2D temperature imaging and local magnetic hyperthermia by using Ln3+-bearing polymeric micelles.Chapter 7 is dedicated to the study of local hyperthermia by means of intracellular 2D temperature imaging of Ln3+-bearing iron oxide nanoparticles ac magnetic field application to cell cultures.<br /

Bis{2-[(1H-pyrrol-2-yl)methyl­imino­meth­yl]phenolato-κ2 N,O}zinc(II)

In the title compound, [Zn(C12H11N2O)2], the ZnII atom, lying on an inversion center, is coordinated by two O atoms and two N atoms from two salicylal Schiff base ligands in a distorted square-planar geometry. A three-dimensional network is formed by inter­molecular C—H⋯N hydrogen bonds and C—H⋯π contacts

Application-Driven AI Paradigm for Human Action Recognition

Human action recognition in computer vision has been widely studied in recent years. However, most algorithms consider only certain action specially with even high computational cost. That is not suitable for practical applications with multiple actions to be identified with low computational cost. To meet various application scenarios, this paper presents a unified human action recognition framework composed of two modules, i.e., multi-form human detection and corresponding action classification. Among them, an open-source dataset is constructed to train a multi-form human detection model that distinguishes a human being's whole body, upper body or part body, and the followed action classification model is adopted to recognize such action as falling, sleeping or on-duty, etc. Some experimental results show that the unified framework is effective for various application scenarios. It is expected to be a new application-driven AI paradigm for human action recognition