AI Module Research Articles

Streamlining Distribution Routes Using the Language Model of Artificial Intelligence

This article addresses the use of artificial intelligence for the needs of effective, sustainable development in logistics and its components. The subject of this article is to highlight the possibility of processing optimization methods using an artificial intelligence module. The goal is to determine whether the AI module can replicate the same, or at least have a similar result, as the traditional optimization methods used in practice. The challenge involves constantly identifying reserves in already highly sophisticated micro-logistics systems using modern commercial means of artificial intelligence. Applying artificial intelligence to elements of a company’s micro-logistics model is a new approach. This article aims to determine whether artificial intelligence can reduce costs through calculations in a specific area defined for it. By optimizing distribution routes using ChatGPT-3.5, we significantly reduced the total distance traveled, leading to substantial savings in transportation costs. This optimization led to a significant improvement in the efficiency of logistic processes and considerable cost savings. This result demonstrates that artificial intelligence can be an effective tool for solving complex logistic tasks. The possibilities of effectively sustainable logistics development with the help of artificial intelligence lie not only in the quality of the achieved outputs but also in the speed of the calculations and the procedures for solving defined project tasks. It follows from this definition that artificial intelligence will continue to play an essential role in the defined field of logistics in the future.

Let’s Discover More API Relations: A Large Language Model-based AI Chain for Unsupervised API Relation Inference

APIs have intricate relations that can be described in text and represented as knowledge graphs to aid software engineering tasks. Existing relation extraction methods have limitations, such as limited API text corpus and affected by the characteristics of the input text. To address these limitations, we propose utilizing large language models (LLMs) (e.g., gpt-3.5) as a neural knowledge base for API relation inference. This approach leverages the entire Web used to pre-train LLMs as a knowledge base and is insensitive to the context and complexity of input texts. To ensure accurate inference, we design an AI chain consisting of three AI modules: API Fully Qualified Name (FQN) Parser, API Knowledge Extractor, and API Relation Decider. The accuracy of the API FQN Parser and API Relation Decider is 0.81 and 0.83, respectively. Using the generative capacity of the LLM and our approach’s inference capability, we achieve an average F1 value of 0.76 under the three datasets, significantly higher than the state-of-the-art method’s average F1 value of 0.40. Compared to the original CoT and modularized CoT methods, our AI chain design has improved the performance of API relation inference by 71% and 49%, respectively. Meanwhile, the prompt ensembling strategy enhances the performance of our approach by 32%. The API relations inferred by our method can be further organized into structured forms to provide support for other software engineering tasks.

P-164 The impact of oocyte morphological abnormalities on fertilization rate and blastocyst quality

Abstract Study question Do distinct morphological abnormalities in oocytes have an adverse effect on both fertilization rates and the quality of resulting blastocyst? Summary answer While most oocyte morphological abnormalities did not significantly affect outcomes, dark cytoplasmic color correlated with lower fertilization rates and the absence of good quality blastocysts What is known already Variability exists in the fertilization and developmental potential of mature (metaphase II - MII) human oocytes. A high-quality MII oocyte is characterized by a spherical shape, homogeneous cytoplasm, smooth appearance, a uniform zona pellucida (ZP), a normal-sized perivitelline space (PVS), and a single unfragmented polar body (PB). Dysmorphisms, such as variations in cytoplasmic features and anomalies in PVS size and PB regularity may occur. There are conflicting reports on the relationship between oocyte morphology and IVF outcomes. A recent meta-analysis highlighted associations between poor fertilization potential and specific dysmorphisms, including large/irregular PB, a large PVS, refractile bodies, and vacuoles. Study design, size, duration This study analyzed 367 MII oocytes obtained from 40 young patients. The fertilization rate (FR) and blastocyst quality (BQ) were compared among 132 dysmorphic and 235 normal MII oocytes (from October 2023 to January 2024). Dysmorphic oocytes were categorized as having a non-spherical shape, large PVS, PVS debris, abnormal ZP, fragmented PB, excess cytoplasmic granules, vacuoles, and dark cytoplasmic color. Participants/materials, setting, methods All MII oocyte denuded were examined under the microscope at x-400 magnification. Dysmorphic and normal oocytes were separated, injected, and cultured individually until day 5 under conditions of 37oC and 6% CO2. Fertilization was assessed 14 to 18 hours post-injection, and subsequent embryonic development was evaluated on day 2 and day 5. Blastocysts graded 4AA to 4BC are considered good quality, while those graded 4CC to 3CC are classified as fair quality. Main results and the role of chance The mean age of patients was 24.14 ± 3.77. In total, 36% of all oocytes exhibit at least one dysmorphism (n = 132). From the overall dataset analyzed, there was no significant difference (p > 0.9999) in FR between the normal 77.87% (183/235) and dysmorphic group of oocytes 78.03 (103/132). The odd ratio and 95% confidence interval are 0.9909 and 0.5957 to 1.633 respectively. However, this was not the same for oocytes with dark color cytoplasm. The presence of this dysmorphism significantly lowered FR (p = 0.0176) with an odd ratio of 5.544 (95% CI: 1.419 to 21.67). Similarly, the result showed no statistically significant difference (p = 0.1073) in the likelihood of obtaining a fair quality blastocyst between the normal oocytes 23.40% (55/235) and dysmorphic group of oocytes 15.79 (21/133). This was also the same for the likelihood of obtaining good quality blastocyst. There was no statistically significant difference (p = 0.1536) between the normal group 20.00% (47/235) and the dysmorphic group 26.52 (35/132). Oocyte with dark color cytoplasm gave no good quality blastocyst. Limitations, reasons for caution The exclusive use of oocytes from young patients may limit the generalizability of findings to a broader population of IVF patients. Also, the sample size may limit the study’s ability to detect subtle associations. A reason for caution when interpreting this study is its focus on morphological assessments alone. Wider implications of the findings The knowledge from our findings may contribute to optimizing oocyte selection strategies for intracytoplasmic sperm injection. Also, the knowledge could potentially be integrated into AI modules for oocyte morphological assessment. However, more studies would be required to develop a robust and scalable system Trial registration number Not applicable

Design and Implementation of an ARM-Based AI Module for Ectopic Beat Classification Using Custom and Structural Pruned Lightweight CNN

Design and Implementation of an ARM-Based AI Module for Ectopic Beat Classification Using Custom and Structural Pruned Lightweight CNN

85‐1: Invited Paper: A Novel OLED Material Discovery based on AI Technology

We report a novel OLED material discovery process and several applications based on AI technology. This process in which six AI modules that generate molecular structures with active learning algorithm, predict multiple properties, analyze novelty, predict synthetic scheme, predict relative synthesizability and predict device characteristics are linked one after another. Also, we introduce some cases in which materials designed by this process were actually synthesized and applied to devices for evaluation to confirm the improvement of characteristics.

Enhancing AI Education at an MSI: A Design-Based Research Approach

While students are often passionate about their chosen fields, they often have limited awareness of the profound impact of AI technologies on their professions. In order to advance efforts in building subject-relevant AI literacy among undergraduate students studying Computer Science and non-Computer Science (Criminal Justice and Forensic Science) it is imperative to engage in rigorous efforts to develop and study curricular infusion of Artificial Intelligence topics. Using a Design-Based Research model, the project team and the external evaluators studied the first iteration of the module development and implementation. Using data collected through surveys, focus groups, critical review, and reflection exercises the external evaluation team produced findings that informed the project team in revising and improving their materials and approach for the second iteration. These efforts can help educators and the AI module developers tailor their AI curriculum to address these specific areas, ensuring that students develop a more accurate understanding of applications of AI in their future career field.

AI-Driven Dim-Light Adaptive Camera (DimCam) for Lunar Robots

Abstract. The past decade has been a boom in lunar exploration. China, India, Japan and other countries have successfully landed landers or rovers on the lunar surface (Wu et al., 2014, 2018, 2020; Prasad et al., 2023). Future missions to explore the Moon are focusing on the lunar south pole (Peña-Asensio et al., 2024). The solar altitude angle at the lunar south pole is extremely low, resulting in low solar irradiance and large areas often in dim light or shadows. The permanently shadowed regions (PSRs) at the lunar south pole are also likely containing substantial amounts of water ice (Li et al., 2018). Future lunar robots exploring the lunar south pole will need to operate in low light or shadowed regions, making camera sensors sensitive to the dim-light environments necessary for these robots. Common night vision sensors usually use near-infrared cameras. However, sensors based on passive infrared technology have image resolution limited by several factors, including the intensity of infrared radiation emitted by the object, the sensitivity of the camera, and the performance of the optical system. For instance, thermal imagers typically have a resolution of 388*284 pixels only. We have developed a dim-light adaptive camera (DimCam) that is ultra-sensitive to the varying illumination conditions driven by AI to achieve high-definition imaging of 1080P or above, for future lunar robots operating in shadows or dim-light regions. The DimCam integrates two starlight-level ultra-sensitive imaging sensors connected by a rigid base to provide stereo vision in low illumination environment. An AI edge computing unit is embedded inside the DimCam to adaptively denoise and enhance image quality. The AI module uses an end-to-end image denoising network to identify and remove noises in the images more accurately by utilizing depth information from the stereo sensors. Compared with traditional monocular denoising algorithms, the denoising network based on stereo vision can significantly improve denoising effects and efficiency by enhancing the signal-to-noise ratio of the data input in the front end. The superposition of overlapping scenes can be regarded as a delayed exposure. Concurrently, the residual analysis of the aligned images aids in noise identification. In addition, for pixels obscured by noise, more accurate pixel values can be restored through interpolation or replacement using depth information obtained from the stereo sensors. Subsequently, a pre-trained lightweight deep network modified from Zero-DCE (Guo et al., 2020) is used for image quality enhancement in terms of brightness and contrast, providing high-quality images even in low-light environments for subsequent applications, such as positioning and navigation of robots, 3D mapping of the surrounding environment, and autonomous driven. We have tested the DimCam in a simulated environment in the laboratory, and the results show that the DimCam has promising performances and great potential for various applications.

Abstract PS03-07: Morphometric signature identifies ductal carcinoma in situ of the breast with low risk of progression to invasive breast cancer

Abstract Background. Ductal carcinoma in situ (DCIS) is a frequently found precursor of invasive breast cancer (IBC). However, the majority of DCIS will never progress to IBC. As we cannot distinguish yet which DCIS will remain indolent (‘harmless’) from those that might progress in the future to IBC, almost all women with DCIS are intensively treated by surgery, often followed by radiotherapy. This brings an urgent clinical need to learn distinguishing harmless from potentially progressive DCIS to save many women with indolent DCIS the burden of unnecessary overtreatment. Aim. We aimed to investigate if the geometry and spatial configuration of DCIS ducts in Hematoxylin-Eosin (H&E) stained tissue sections are related to the risk of progression of DCIS to ipsilateral IBC (iIBC). Methods. We obtained data from a population-based cohort of women diagnosed with primary DCIS between 1989 and 2004 in the Netherlands, treated with breast conserving surgery (BCS) only and a median follow-up time of 12 years. A nested case-control study (n=689) was designed in which patients diagnosed with iIBC recurrences during follow-up were considered as “cases” (n= 226) and those with no subsequent iIBC as “controls” (n=463). The DCIS and stroma regions were digitally annotated on H&E-stained whole slide images (WSIs) by a pathologist as ground truth for the deep learning neural network of HALO AI module (IndicaLabs). We developed a computational pipeline to automatically detect and measure stroma areas, DCIS ducts, and the nucleus of their cells. We validated the accuracy of DCIS detection in H&Es WSIs from an external study, in which DCIS regions were digitally annotated by an independent pathologist (Translational Breast Cancer Research Consortium, TBCRC). We classified cases and controls according to morphological measurements using logistic ridge regression with double-loop cross-validation, followed by hierarchical clustering. The risk of subsequent iIBC after primary DCIS diagnosis was evaluated by multivariate Cox proportional hazards models. Results. The accuracy of DCIS detection performed by the computational pipeline was compared with pathologist annotations in 20 slides from the TBCRC study. Results showed a satisfactory DCIS overlap area agreement of 0.76 (0.68 – 0.83). We applied the DCIS computational pipeline on the case-control series. We obtained 15 morphological measurements for each DCIS duct, such as duct area, cell density, distance between ducts, average nucleus area, etc. We calculated 8 distribution parameters from each measurement in each WSI, including median and range. After leaving out redundant variables, 55 unique morphometric variables were obtained, representing the heterogeneity of DCIS ducts per WSI. The c lassifier revealed a median area-under the curve (AUC) of 0.66 (0.55-0.77) to predict 5-years free of iIBC, 0.59 (0.50-0.67) to predict 10-years and 0.60 (0.52-0.68) to predict 15-years. The 30 variables with the highest association with outcome were used to build four morphometric signatures. Signature number 1, which is characterized by lesions with small-sized ducts, a lower number of cells and a lower DCIS/stroma area ratio, showed a significant lower risk of developing iIBC compared to the other three signatures in a multivariate Cox regression model including grade, ER, COX-2 and HER2 expression: HR = 0.56 (0.28-0.78 95%CI). Conclusion. We developed a computational pipeline able to detect and measure DCIS ducts in H&E WSIs with high accuracy and reproducibility. DCIS lesions presenting the morphometric signature of small-sized DCIS ducts have a very low chance to progress to invasive breast cancer. After successful validation, our morphometric method will serve as a robust and easy to implement biomarker for de-escalation strategies in DCIS, and as such, could limit unnecessary overtreatment in the near future. Citation Format: Marcelo Sobral-Leite, Simon Castillo, Shiva Vonk, Xenia Melillo, Noomie Lam, Brandi de Bruijn, Yeman Hagos, Joyce Sanders, Mathilde Almekinders, Lindy Visser, Emilie Groen, Carolien Van der Borden, Petra Kristel, Ercan Caner, Leyla Azarang, Yinyin Yuan, Renee Menezes, Esther Lips, Jelle Wesseling, Grand Challenge PRECISION Consortium. Morphometric signature identifies ductal carcinoma in situ of the breast with low risk of progression to invasive breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS03-07.

Digital Datasheet model: enhancing value of AI digital platforms

Digital AI platforms/marketplaces play a pivotal role in providing artificial intelligence applications for the manufacturing market. However, existing research on how AI platforms integrate and deploy AI solutions is limited. Additionally, Europe is grappling with the absence of a functional ecosystem capable enhancing the value generated by these AI technologies. This work proposes a digital datasheet model designed to facilitate value exchange among digital AI platforms/marketplaces, module providers, and end-users, enabling a new type of interaction for integrating AI assets, developing adaptable AI systems, meeting customer needs. The applicability is tested with an AI module of the Kitt4sme project.

Single-Belt Versus Split-Belt: Intelligent Treadmill Control via Microphase Gait Capture for Poststroke Rehabilitation.

Stroke is the leading long-term disability and causes a significant financial burden associated with rehabilitation. In poststroke rehabilitation, individuals with hemiparesis have a specialized demand for coordinated movement between the paretic and the nonparetic legs. The split-belt treadmill can effectively facilitate the paretic leg by slowing down the belt speed for that leg while the patient is walking on a split-belt treadmill. Although studies have found that split-belt treadmills can produce better gait recovery outcomes than traditional single-belt treadmills, the high cost of split-belt treadmills is a significant barrier to stroke rehabilitation in clinics. In this article, we design an AI-based system for the single-belt treadmill to make it act like a split-belt by adjusting the belt speed instantaneously according to the patient's microgait phases. This system only requires a low-cost RGB camera to capture human gait patterns. A novel microgait classification pipeline model is used to detect gait phases in real time. The pipeline is based on self-supervised learning that can calibrate the anchor video with the real-time video. We then use a ResNet-LSTM module to handle temporal information and increase accuracy. A real-time filtering algorithm is used to smoothen the treadmill control. We have tested the developed system with 34 healthy individuals and four stroke patients. The results show that our system is able to detect the gait microphase accurately and requires less human annotation in training, compared to the ResNet50 classifier. Our system "Splicer" is boosted by AI modules and performs comparably as a split-belt system, in terms of timely varying left/right foot speed, creating a hemiparetic gait in healthy individuals, and promoting paretic side symmetry in force exertion for stroke patients. This innovative design can potentially provide cost-effective rehabilitation treatment for hemiparetic patients.

Anomaly Detection with Decision Trees for AI Assisted Evaluation of Signal Integrity on PCB Transmission Lines

Abstract. Printed circuit board (PCB) design can be supported to a high degree by adding AI modules to the design system. Predictions from these modules can be made available to the designer in order to speed up circuit design and make it more efficient. Problems regarding signal integrity (SI) can be detected in time by providing hints on component connection or routing. However, the optimization and ML methods used in this context are usually very sophisticated (e.g., Bayesian optimization). Therefore, the design parameters provided by the AI modules must be accepted without further insights (for the experienced as well as the inexperienced designer). In this paper, a decision tree for anomaly detection and SI verification is presented, which by nature of this algorithm provides insights to the decisions made to obtain the proposed design parameters. Using a point-to-point (P2P) network as an example, the prediction accuracy of the AI model is investigated. It is shown that assessing SI effects with a decision tree provides a simple approach to obtain the suggested design. Furthermore, the predictions of the decision tree can be verified against the design rules.

Dynamic optimisation of CO2 electrochemical reduction processes driven by intermittent renewable energy: Hybrid deep learning approach

The increasing demand for net zero solutions has prompted the exploration of electrochemical CO2 reduction reaction (eCO2RR) systems powered by renewable energy sources. Here, we present a comprehensive AI-enabled framework for the adaptive optimisation of the dynamic eCO2RR processes in response to the intermittent renewable energy supply. The framework includes (1). a Bi-LSTM (bidirectional long-short-term memory) to predict the meteorological data for renewable energy input; (2). a deep learning surrogate model to predict the eCO2RR process performance; and (3). a NSGA-II algorithm for multi-objective optimisation, targeting the trade-off of the single-pass Faraday efficiency (FE), product yield (PY) and conversion. The framework seamlessly integrates the three different AI modules, enabling adaptive optimisation of the eCO2RR system composed of electrolyser stacks and renewable energy sources, and providing insights into system's performance and feasibility under real-world conditions.

Intelligent Monitoring System with Privacy Preservation Based on Edge AI.

Currently, the trend of elderly people living alone is rising due to rapid aging and shifts in family structures. Accordingly, the efficient implementation and management of monitoring systems tailored for elderly people living alone have become paramount. Monitoring systems are generally implemented based on multiple sensors, and the collected data are processed on a server to provide monitoring services to users. Due to the use of multiple sensors and a reliance on servers, there are limitations to economical maintenance and a risk of highly personal information being leaked. In this paper, we propose an intelligent monitoring system with privacy preservation based on edge AI. The proposed system achieves cost competitiveness and ensures high security by blocking communication between the camera module and the server with an edge AI module. Additionally, applying edge computing technology allows for the efficient processing of data traffic. The edge AI module was designed with Verilog HDL and was implemented on a field-programmable gate array (FPGA). Through experiments conducted on 6144 frames, we achieved 95.34% accuracy. Synthesis results in a 180 nm CMOS technology indicated a gate count of 1516 K and a power consumption of 344.44 mW.

Developing a Single-Player Sci-Fi First-Person Shooter in Unity3D

First-Person Shooter is currently one of most popular game genres. However, most of the shooters are realistic and multiplayer oriented. Besides, there’s no single-player oriented FPS framework made for Unity3D which remains a small blank in Sci-Fi single player FPS game area in recent years. So, this paper developed a game named What Happened to Site-13? using Unity3D which is a realistic Sci-Fi FPS which is single-player oriented. The game contains a gamepad-oriented FPS Controller, Story-telling module, AI Module, an in-game map editor and scene with realistic sci-fi art style. Through tests with testers, to test if the game is immersive, and the combat loop is comfortable, the visuals meet expectations. After tests and validation, it was proved to achieve target art style, visual experience and combat loop, it also was proven to be immersive. Also, it can serve as a framework for FPS games after validation with a group of students.

Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study

BackgroundArtificial intelligence (AI)-enabled applications are increasingly being used in providing healthcare services, such as medical imaging support. Sufficient and appropriate education for medical imaging professionals is required for successful AI adoption. Although, currently, there are AI training programmes for radiologists, formal AI education for radiographers is lacking. Therefore, this study aimed to evaluate and discuss a postgraduate-level module on AI developed in the UK for radiographers.MethodologyA participatory action research methodology was applied, with participants recruited from the first cohort of students enrolled in this module and faculty members. Data were collected using online, semi-structured, individual interviews and focus group discussions. Textual data were processed using data-driven thematic analysis.ResultsSeven students and six faculty members participated in this evaluation. Results can be summarised in the following four themes: a. participants’ professional and educational backgrounds influenced their experiences, b. participants found the learning experience meaningful concerning module design, organisation, and pedagogical approaches, c. some module design and delivery aspects were identified as barriers to learning, and d. participants suggested how the ideal AI course could look like based on their experiences.ConclusionsThe findings of our work show that an AI module can assist educators/academics in developing similar AI education provisions for radiographers and other medical imaging and radiation sciences professionals. A blended learning delivery format, combined with customisable and contextualised content, using an interprofessional faculty approach is recommended for future similar courses.

AI-Assisted Failure Location Platform for Optical Network

In the paper, we applied the customized AI module to the OTDR device and, combined with the optical power monitoring module, realized the AI-assisted optical network fault location mechanism for the high-density interconnection scenario of data centers. The mechanism can make full use of the data from optical links. Based on the link data, the AI module can predict the links that may fail, and then the target links will be monitored by the optical power module. The mechanism can quickly locate and respond to faulty links. Through the test, the introduction of an AI model can improve the average fault detection efficiency of the link by 98.41%.

IoMT with Deep CNN: AI-Based Intelligent Support System for Pandemic Diseases

The Internet of Medical Things (IoMT) is an extended version of the Internet of Things (IoT). It mainly concentrates on the integration of medical things for servicing needy people who cannot get medical services easily, especially rural area people and aged peoples living alone. The main objective of this work is to design a real time interactive system for providing medical services to the needy who do not have a sufficient medical infrastructure. With the help of this system, people will get medical services at their end with minimal medical infrastructure and less treatment cost. However, the designed system could be upgraded to address the family of SARs viruses, and for experimentation, we have taken COVID-19 as a test case. The proposed system comprises of many modules, such as the user interface, analytics, cloud, etc. The proposed user interface is designed for interactive data collection. At the initial stage, it collects preliminary medical information, such as the pulse oxygen rate and RT-PCR results. With the help of a pulse oximeter, they could get the pulse oxygen level. With the help of swap test kit, they could find COVID-19 positivity. That information is uploaded as preliminary information to the designed proposed system via the designed UI. If the system identifies the COVID positivity, it requests that the person upload X-ray/CT images for ranking the severity of the disease. The system is designed for multi-model data. Hence, it can deal with X-ray, CT images, and textual data (RT-PCR results). Once X-ray/CT images are collected via the designed UI, those images are forwarded to the designed AI module for analytics. The proposed AI system is designed for multi-disease classification. It classifies the patients affected with COVID-19 or pneumonia or any other viral infection. It also measures the intensity level of lung infection for providing suitable treatment to the patients. Numerous deep convolution neural network (DCNN) architectures are available for medical image classification. We used ResNet-50, ResNet-100, ResNet-101, VGG 16, and VGG 19 for better classification. From the experimentation, it observed that ResNet101 and VGG 19 outperform, with an accuracy of 97% for CT images. ResNet101 outperforms with an accuracy of 98% for X-ray images. For obtaining enhanced accuracy, we used a major voting classifier. It combines all the classifiers result and presents the majority voted one. It results in reduced classifier bias. Finally, the proposed system presents an automatic test summary report textually. It can be accessed via user-friendly graphical user interface (GUI). It results in a reduced report generation time and individual bias.

KDE-GAN: A multimodal medical image-fusion model based on knowledge distillation and explainable AI modules

BackgroundAs medical images contain sensitive patient information, finding a publicly accessible dataset with patient permission is challenging. Furthermore, few large-scale datasets suitable for training image-fusion models are available. To address this issue, we propose a medical image-fusion model based on knowledge distillation (KD) and an explainable AI module-based generative adversarial network with dual discriminators (KDE-GAN). MethodKD reduces the size of the datasets required for training by refining a complex image-fusion model into a simple model with the same feature-extraction capabilities as the complex model. The images generated by the explainable AI module show whether the discriminator can distinguish true images from false images. When the discriminator precisely judges the image based on the key features, the training can be stopped early, reducing overfitting and the amount of data required for training. ResultsBy training using only small-scale datasets, the trained KDE-GAN can generate clear fused images. KDE-GAN fusion results were evaluated quantitatively using five metrics: spatial frequency, structural similarity, edge information transfer factor, normalized mutual information, and nonlinear correlation information entropy. ConclusionExperimental results show that the fused images generated by KDE-GAN are superior to state-of-the-art methods, both subjectively and objectively.

TNT Loss: A Technical and Nontechnical Generative Cooperative Energy Loss Detection System.

This paper describes an electricity technical/nontechnical loss detection method capable of loss type identification, classification, and location. Several technologies are implemented to obtain that goal: (i) an architecture of three generative cooperative AI modules and two additional non-cooperative AI modules for data knowledge sharing is proposed, (ii) new expert consumption-based knowledge of feature collaboration of the entire consumption data are embedded as features in an AI classification algorithm, and (iii) an anomaly pooling mechanism that enables one-to-one mapping of signatures to loss types is proposed. A major objective of the paper is an explanation of how an exact loss type to signature mapping is obtained simply and rapidly, (iv) the role of the reactive energy load profile for enhancing signatures for loss types is exemplified, (v) a mathematical demonstration of the quantitative relationship between the features space to algorithm performance is obtained generically for any algorithm, and (vi) a theory of “generative cooperative modules” for technical/nontechnical loss detection is located and mapped to the presented system. The system is shown to enable high-accuracy technical/nontechnical loss detection, especially differentiated from other grid anomalies that certainly exist in field conditions and are not tagged in the universal datasets. The “pooling” architecture algorithm identifies all other loss types, and a robotic process automation module obtains loss type localization. The system feeds from the entire smart metering data, not only the energy load profile. Other solutions, such as a stand-alone algorithm, have difficulty in obtaining low false positive in field conditions. The work is tested experimentally to demonstrate the matching of experiment and theory.

Introducing the Monitoring Equipment Mask Environment.

Filter face masks are Respiratory Protective Equipment designed to protect the wearer from various hazards, suit various health situations, and match the specific requirements of the wearer. Current traditional face masks have several limitations. In this paper, we present (ME), the Monitoring Equipment Mask Environment: an innovative reusable 3D-printed eco-sustainable mask with an interchangeable filter. (ME) is equipped with multiple vital sensors on board, connected to a system-on-a-chip micro-controller with computational capabilities, Bluetooth communication, and a rechargeable battery that allows continuous monitoring of the wearer’s vital signs. It monitors body temperature, heart rate, and oxygen saturation in a non-invasive, strategically positioned way. (ME) is accompanied by a mobile application that provides users’ health information. Furthermore, through Edge Computing Artificial Intelligence (Edge AI) modules, it is possible to detect an abnormal and early symptoms linked to possible pathologies, possibly linked to the respiratory or cardiovascular tract, and therefore perform predictive analysis, launch alerts, and recommendations. To validate the feasibility of embedded in-app Edge AI modules, we tested a machine learning model able to distinguish COVID-19 versus seasonal influenza using only vital signs. By generating new synthetic data, we confirm the highly reliable performances of such a model, with an accuracy of .