Machine Learning Applied to Problem-Solving in Medical Applications

Physical health plays an important role in overall well-being of the human beings. It is the most observed dimension of health among others such as social, intellectual, emotional, spiritual and environmental dimensions. Due to exponential increase in the development of wireless communication techniques, Internet of Things (IoT) has effectively penetrated different aspects of human lives. Healthcare is one of the dynamic domains with ever-growing demands which can be met by IoT applications. IoT can be leveraged through several health service offerings such as remote health and monitoring services, aided living, personalized treatment, and so on. In this scenario, Deep Learning (DL) models are employed in proficient disease diagnosis. The current research work presents a new IoT-based physical health monitoring and management method using optimal Stacked Sparse Denoising Autoencoder (SSDA) technique i.e., OSSDA. The proposed model utilizes a set of IoT devices to collect the data from patients. Imbalanced class problem poses serious challenges during disease diagnosis process. So, the OSSDA model includes Synthetic Minority Over-Sampling Technique (SMOTE) to generate artificial minority class instances to balance the class distribution. Further, the hyperparameter settings of the OSSDA model exhibit heavy influence upon the classification performance of SSDA technique. The number of hidden layers, sparsity, and noise count are determined by Sailfish Optimizer (SFO). In order to validate the effectiveness and performance of the proposed OSSDA technique, a set of experiments was conducted on diabetes and heart disease datasets. The simulation results portrayed a proficient diagnostic outcome from OSSDA technique over other methods. The proposed method achieved the highest accuracy values i.e., 0.9604 and 0.9548 on the applied heart disease and diabetes datasets respectively.     

基于Plant Simulation的智能制造测试床仿真模型 设计与应用

为了验证智能制造测试床的整体布局设计和制造工艺在生产运营环境下的合理性，利用西门子Plant Simulation工厂仿真软件进行生产节拍和设备利用率分析。首先，建立了按订单生产运行的制造生产线二维仿真模型，根据生产要求对二维仿真模型进行输入参数设定，并根据二维仿真模型运行后得到的分析统计结果进行工艺布局、物流布局和制造策略的优化。然后，在二维仿真模型基础上，为了让仿真模型在建设阶段和运营阶段具有更直观的展示效果，基于原三维设计图，在三维仿真模型中替换或导入各对象的三维模型，并运用Simltalk语言编写精细的三维位置坐标和动作路径动画程序，对仿真模型进行三维效果优化。结果表明优化后的智能制造测试床三维仿真模型可以实现与实际生产一致的业务运行模式，它具有更形象、逼真的动态运行展示效果。仿真结果可指导智能制造测试床的建设与优化，且在测试床投入运营后，仿真模型还能够接受运营数据以对测试床进行持续迭代的优化。提出的仿真模型设计方法为实现智能制造测试床的“数字孪生”奠定了基础。     

Design and application of Internet of things-based warehouse management system for smart logistics

Warehouse operations need to change due to the increasing complexity and variety of customer orders. The demand for real-time data and contextual information is requried because of the highly customised orders, which tend to be of small batch size but with high variety. Since the orders frequently change according to customer requirements, the synchronisation of purchase orders to support production to ensure on-time order fulfilment is of high importance. However, the inefficient and inaccurate order picking process has adverse effects on the order fulfilment. The objective of this paper is to propose an Internet of things (IoT)-based warehouse management system with an advanced data analytical approach using computational intelligence techniques to enable smart logistics for Industry 4.0. Based on the data collected from a case company, the proposed IoT-based WMS shows that the warehouse productivity, picking accuracy and efficiency can be improved and it is robust to order variability.     

Performance measurement using overall equipment effectiveness (OEE): literature review and practical application discussion

The quest for improving productivity in the current global competitive environment has led to a need for rigorously defined performance-measurement systems for manufacturing processes. In this paper, overall equipment effectiveness (OEE) is described as one such performance-measurement tool that measures different types of production losses and indicates areas of process improvement. Analysis is done on how OEE has evolved leading to other tools like total equipment effectiveness performance, production equipment effectiveness, overall factory effectiveness, overall plant effectiveness, and overall asset effectiveness. Two industrial examples of OEE application are discussed, and the differences between theory and practice analysed. Finally, a framework for classifying and measuring production losses for overall production effectiveness is proposed. The framework harmonizes the differences between theory and practice and makes possible the presentation of overall production/asset effectiveness that can be customized with the manufacturers needs to improve productivity.     

Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

Applications of internet-of-things (IoT) are increasingly being used in many facets of our daily life, which results in an enormous volume of data. Cloud computing and fog computing, two of the most common technologies used in IoT applications, have led to major security concerns. Cyberattacks are on the rise as a result of the usage of these technologies since present security measures are insufficient. Several artificial intelligence (AI) based security solutions, such as intrusion detection systems (IDS), have been proposed in recent years. Intelligent technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection (FS) techniques to increase classification accuracy by minimizing the number of features selected. On the other hand, metaheuristic optimization algorithms have been widely used in feature selection in recent decades. In this paper, we proposed a hybrid optimization algorithm for feature selection in IDS. The proposed algorithm is based on grey wolf (GW), and dipper throated optimization (DTO) algorithms and is referred to as GWDTO. The proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better performance. On the employed IoT-IDS dataset, the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in the literature to validate its superiority. In addition, a statistical analysis is performed to assess the stability and effectiveness of the proposed approach. Experimental results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.     

卷包机组设备效率评价指标体系的设计与实现

目的 针对烟草行业常见设备卷包机组,提出一种结合OEE的设备效率评价体系,实现对卷包机组设备运行状态的精确、实时、具体的反映.方法 通过分析离散制造与连续制造的差异,在传统离散制造OEE方法基础上,针对连续生产进行适应性改进,提出一套针对卷包机组的设备效率评价指标;在此基础上,通过分析卷包机组各运行单元结构特点,提出一套实时采集设备状态数据与动态分析效率状态的卷包机组设备效率评价体系.结果 将此体系部署于湖北中烟某车间6台卷包机上6个月进行实验验证,结果显示在该体系的支持下,设备净效能平均同比增长7.72％,证明本效率评价结果可以精准、全面、实时地反映设备运行情况和状态背后的影响因素,有助于提升卷烟机组设备效率.结论 文中提出的卷包机组设备效率评价指标体系使用户可以针对性、快速地调整设备运行状态,实现了精准管理,提高了生产作业效率,具有实际的推广价值.     

Optimizing OEE,productivity and production cost for improving sales volume in an automobile industry through TPM: a case study

The aim of this paper is to investigate how increase in sales volume has evolved by improving overall equipment effectiveness (OEE) of machines, plant productivity and production cost through total productive maintenance (TPM) initiatives in a reputed tractors manufacturing industry in India. In the present scenario of global competitive market, the manufacturing industry needs to improve their operational performance for surviving and prospering. TPM is practised by industry as a business tool for rapid and continuous improvement in its manufacturing capabilities. OEE offers a powerful control tool to overcome production deficiencies and operational performance constrains. Productivity and manufacturing cost are also crucial operational measures to analyse the manufacturing performance. In this study, evaluation and analysis of the methodology adopted for improving sales volume through TPM initiatives was carried out using an interactive research approach. The industrial example on the application of OEE tool demonstrates that it has a remarkable potential to enhance the equipment effectiveness. The empirical findings of the study reveal that augmented OEE and productivity, and reduced production cost resulted to double the sales revenue and triple the profit within a period of three years. The industry also achieved notably tangible and intangible benefits with the TPM implementation.     

An IoT-Cloud Based Intelligent Computer-Aided Diagnosis of Diabetic Retinopathy Stage Classification Using Deep Learning Approach

Diabetic retinopathy (DR) is a disease with an increasing prevalence and the major reason for blindness among working-age population. The possibility of severe vision loss can be extensively reduced by timely diagnosis and treatment. An automated screening for DR has been identified as an effective method for early DR detection, which can decrease the workload associated to manual grading as well as save diagnosis costs and time. Several studies have been carried out to develop automated detection and classification models for DR. This paper presents a new IoT and cloud-based deep learning for healthcare diagnosis of Diabetic Retinopathy (DR). The proposed model incorporates different processes namely data collection, preprocessing, segmentation, feature extraction and classification. At first, the IoT-based data collection process takes place where the patient wears a head mounted camera to capture the retinal fundus image and send to cloud server. Then, the contrast level of the input DR image gets increased in the preprocessing stage using Contrast Limited Adaptive Histogram Equalization (CLAHE) model. Next, the preprocessed image is segmented using Adaptive Spatial Kernel distance measure-based Fuzzy C-Means clustering (ASKFCM) model. Afterwards, deep Convolution Neural Network (CNN) based Inception v4 model is applied as a feature extractor and the resulting feature vectors undergo classification in line with the Gaussian Naive Bayes (GNB) model. The proposed model was tested using a benchmark DR MESSIDOR image dataset and the obtained results showcased superior performance of the proposed model over other such models compared in the study.     

Automatic translation of plant data into management performance metrics: a case for real-time and predictive production control

A scalable and repeatable solution for linking shop-floor control system to a discrete event simulation (DES) model is presented. The key objective is to automatically translate the real-time data from the control system (e.g. supervisory control and data acquisition, SCADA) into KPI transfer functions of the production process. Such a seamless translation allows for the integration of engineering data emitted at plant level to higher level information system for decision-making. The solution provides a platform for researchers and practitioners to utilise the capabilities of real-time DAQ and control with that of discrete event simulation to accurately measure the key manufacturing systems performance metrics. In addition to the real-time capabilities, the predictive capabilities of the solution provide the managers to look ahead and to conduct What-if scenarios. Such capability enables line management to optimise performance and predict destabilising factors in the system ahead of time. A fully operational version of the designed solution has been deployed in a brewery’s live production system for the first time. The brewhouse production line model measures the utilisation of resources, Overall Equipment Effectiveness, and Overall Line Effectiveness in real-time and fast-forward mode simulation. The results of the predictive models (What-if-Scenarios) have been validated and verified by statistical means and direct observations. The accuracy of the estimated parameters is highly satisfactory.     

Optimized Tuned Deep Learning Model for Chronic Kidney Disease Classification

In recent times, Internet of Things (IoT) and Cloud Computing (CC) paradigms are commonly employed in different healthcare applications. IoT gadgets generate huge volumes of patient data in healthcare domain, which can be examined on cloud over the available storage and computation resources in mobile gadgets. Chronic Kidney Disease (CKD) is one of the deadliest diseases that has high mortality rate across the globe. The current research work presents a novel IoT and cloud-based CKD diagnosis model called Flower Pollination Algorithm (FPA)-based Deep Neural Network (DNN) model abbreviated as FPA-DNN. The steps involved in the presented FPA-DNN model are data collection, preprocessing, Feature Selection (FS), and classification. Primarily, the IoT gadgets are utilized in the collection of a patient’s health information. The proposed FPA-DNN model deploys Oppositional Crow Search (OCS) algorithm for FS, which selects the optimal subset of features from the preprocessed data. The application of FPA helps in tuning the DNN parameters for better classification performance. The simulation analysis of the proposed FPA-DNN model was performed against the benchmark CKD dataset. The results were examined under different aspects. The simulation outcomes established the superior performance of FPA-DNN technique by achieving the highest sensitivity of 98.80%, specificity of 98.66%, accuracy of 98.75%, F-score of 99%, and kappa of 97.33%.     

Governing the progress of internet-of-things: Ambivalence in the quest of technology exploitation and user rights protection

This paper discusses the dynamics between the pursuit of competitiveness and governance of data security in strengthening the Internet of Things (IoT) readiness in developing economies using Malaysia as a case study. It explores the potential of the IoT regulatory framework in guarding the privacy and interests of IoT users. This paper also reveals the collaborative model of technology push-market pull for technological capabilities development as well as the measures that uphold the principles of good privacy practice. The model incorporates privacy-by-design measures that would result in higher user confidence in this emerging technology, which is vital to a healthy IoT ecosystem. Through the collaborative model of Penang as evidence, our findings indicate that Malaysia seeks to create a structure that fosters technology push-market pull forces for IoT technological capabilities development. While the model paves a co-evolutionary path for diffusion and upgrading of IoT, several issues related to the volatility of online data and databases were identified as well as the lack of responsibility and accountability of corporations in handling the sensitive personal data of customers. We see that it is essential for the regulators to play a (more) significant role in safeguarding the interests of IoT users. In this regard, the privacy-by-design, a citizen-centric regulatory framework should be considered in policy reviews in deploying IoT-based competitive promotion initiatives. This paper breaks new ground by elaborating on the common route of IoT technology capabilities development, which is typical in the developing context. While it highlights the common issues that emerge as technology advances, we propose a regulatory framework that features embedded privacy-by-design to protect the interests of the IoT users.     

Review of operational management in intelligent agriculture based on the Internet of Things

This review aims to gain insight into the current research and application of operational management in the area of intelligent agriculture based on the Internet of Things (IoT), and consequently, identify existing shortcomings and potential issues. First, we use the Java application CiteSpace to analyze co-citation networks in the literature related to the operational management of IoT-based intelligent agriculture. From the literature analysis results, we identify three major fields: (1) the development of agricultural IoT (Agri-IoT) technology, (2) the precision management of agricultural production, and (3) the traceability management of agricultural products. Second, we review research in the three fields separately in detail. Third, on the basis of the research gaps identified in the review and from the perspective of integrating and upgrading the entire agricultural industry chain, additional research directions are recommended from the following aspects: The operational management of agricultural production, product processing, and product sale and after-sale service based on Agri-IoT. The theoretical research and practical application of combining operational management theories and IoT-based intelligent agriculture will provide informed decision support for stakeholders and drive the further development of the entire agriculture industry chain.     

Event-driven multi-agent ubiquitous manufacturing execution platform for shop floor work-in-progress management

It has been reported that radio frequency identification (RFID) technology is applied to manufacturing shop floors for capturing and collecting real-time field data. Real-time information visibility and traceability allows decision makers to make better-informed shop floor decisions. It has been a great challenge to process a huge amount of RFID data into useful information for managerial uses. This paper presents an event-driven shop floor work-in-progress (WIP) management platform for creating a ubiquitous manufacturing (UM) environment. The platform aims to monitor and control dynamic production and material handling through RFID-enabled traceability and visibility of shop floor manufacturing processes. The platform provides facilities to process shop floor real-time RFID events and to aggregate actionable and meaningful operational information to support decision-making activities. An information processing mechanism based on a critical event model is proposed to organise real-time field data in various abstract levels for enterprise decisions. A case study at an air conditioner manufacturing company is used to demonstrate how the proposed platform can benefit its shop floor WIP management by showing how production and logistic operators and their supervisors accomplish their tasks.     

Engineering model-based Bayesian monitoring of ramp-up phase of multistage manufacturing process

Process monitoring of full mass production phase of multistage manufacturing processes (MMPs) has been successfully implemented in many applications; however, monitoring of ramp-up phase of MMPs is often more difficult to conduct due to the limited information to establish valid process control parameters (such as mean and variance). This paper focuses on the estimation of the process control parameters used for monitoring scheme design of ramp-up phase of MMPs. An engineering model of variation propagation of an MMP is developed and reconstructed to a linear model, establishing a relationship between the error sources and the variation of product characteristics. Based on the developed linear model, a two-step Bayesian method is proposed to estimate the process control parameters. The performance of the proposed Bayesian method is validated with simulation data and real-world data, and the results demonstrate that the proposed method can effectively estimate process parameters during ramp-up phase of MMP.     

Manufacturing process analysis with support of workflow modelling and simulation

Process analysis is recognized as a major stage in business process reengineering that has developed over the last two decades. Manufacturing process analysis (MPA) is defined as performance analysis of the production process. A manufacturing process analysis framework is outlined with emphasis on linking a company's strategy to operational process. Two issues, namely process modelling and simulation based analysis, are investigated. A compound workflow model (CWM) is proposed to provide graphic presentation of the production process that can be easily understood. Also it can be used directly by simulation to study the impacts of scheduling policy and analyse the process performance. A two-stage simulation analysis method is provided to quantitatively and efficiently define cause-and-effect relations to identify drivers for improvement. The manufacturing environment, PSC (production planning, scheduling and control) factors and the process structure are three main concerns considered in the simulation. An example is discussed in the final part of the paper.     

Energy Efficient Cluster Based Clinical Decision Support System in IoT Environment

Internet of Things (IoT) has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications and embedded devices. The e-healthcare application solely depends on the IoT and cloud computing environment, has provided several characteristics and applications. Prior research works reported that the energy consumption for transmission process is significantly higher compared to sensing and processing, which led to quick exhaustion of energy. In this view, this paper introduces a new energy efficient cluster enabled clinical decision support system (EEC-CDSS) for embedded IoT environment. The presented EEC-CDSS model aims to effectively transmit the medical data from IoT devices and perform accurate diagnostic process. The EEC-CDSS model incorporates particle swarm optimization with levy distribution (PSO-L) based clustering technique, which clusters the set of IoT devices and reduces the amount of data transmission. In addition, the IoT devices forward the data to the cloud where the actual classification procedure is performed. For classification process, variational autoencoder (VAE) is used to determine the existence of disease or not. In order to investigate the proficient results analysis of the EEC-CDSS model, a wide range of simulations was carried out on heart disease and diabetes dataset. The obtained simulation values pointed out the supremacy of the EEC-CDSS model interms of energy efficiency and classification accuracy.     

制造物联网系统动力学建模与分析

动态因素影响生产系统的运作表现,而相应的流程再造和信息共享机制使得充满动态性的生产系统达到统计学意义上的运作最优化。随着系统动力学广泛应用于系统结构和参数的控制与仿真,物联网技术为广泛、实时、精确采集生产系统的动态性提供了使能手段,使得系统动力学可以在实时信息环境下对生产系统的运作表现进行全面、敏捷、精确的仿真和分析,使得制造物联网系统的运作达到最优。本文采用系统动力学构建仿真模型,对比分析物联网技术应用前后的生产系统运作差异,并通过投资回报率分析不同方案的经济可行性,为企业决策提供参考依据。     

Stochastic overall equipment effectiveness

This article focuses on the overall equipment effectiveness (OEE), a key performance indicator typically adopted to support Lean Manufacturing and Total Productive Maintenance. Unfortunately, being a deterministic metric, the OEE only provides a static representation of a process, but fails to capture the real variability of manufacturing performances. To take into account the stochastic nature of the OEE, an approximated procedure based on the application of the Central Limit Theorem is presented: the OEE is considered as a stochastic random variable and its probability density function (pdf) is generated through the aggregation of the pdf of the basic causes of waste. Notwithstanding its approximated nature, the procedure can be applied in most practical cases, since the accuracy is assured provided that the average OEE is lower than 90% and the variability of the losses is high. The validity of the approach has also been confirmed by an industrial application included in this article. The results obtained demonstrate that the stochastic OEE can help in battling variation, for it allows one to identify the hidden losses that account for most of the variability and to estimate the impacts of potential corrective actions in terms of both efficiency and efficacy.     

OEE多维数据分析系统的应用研究

针对生产领域中重要效率评价指标OEE,研究如何用多维的思路和方法来分析.结合OEE的定义、多维分析基本思想,以及当前各类OEE分析软件特点的基础,为实现对海量多维生产数据准确、高效、灵活的分析,提出一种OEE多维数据分析系统.讨论了多维数据分析技术在OEE数据分析系统的研究和应用的设计过程,并对基于Excel VBA开发的OEE多维数据分析系统进行实证研究.     

Performance evaluation of an auction-based manufacturing system using coloured Petri nets

The next generation of manufacturing systems is assumed to be intelligent enough to make decisions and automatically adjust to variations in production demand, shop-floor breakdowns etc. Auction-based manufacturing is a control strategy in which various intelligent entities in the manufacturing system bid themselves, accept bids and make selections among the bids available based on a heuristic. This paper deals with the simulation modelling and performance evaluation of a push-type auction (negotiation) based manufacturing system embedded in a pulltype production system using coloured Petri nets. Three different models of an auction-based manufacturing system have been discussed. This methodology helps in developing systems for real-time control, anticipation of deadlocks, and evaluation of various performance metrics like machine utilization, automated guided vehicle (AGV) utilization, waiting times, work in process (WIP) etc. Various decision-making rules were identified for the real-time control of auction-based manufacturing systems.