Prediction of sedimentation and bank erosion due to the construction of Kahang Dam

River impoundments continue to cause changes to the hydrological regimes of its host river. Thus, assessment and development of tools for better understanding of the sediment dynamics and riverbank erosion downstream the dam will be of great benefit to researchers and policymakers. The present research employs the use of field techniques and estimation models to improve the (i) prediction of suspended sediment concentration, (ii) monitoring riverbank erosion, and (iii) development of Riverbank Erosion Index (RbEI) for downstream Kahang Dam. This research used the Artificial Neural Network (ANN) and ANN with Autoregressive (AR) (NNETAR) in predicting suspended sediment concentration using sediment concentration, discharge and water level as inputs. Similarly, erosion pins were installed on four transects to monitor the riverbank for thirteen months. The results obtained for sediment concentration prediction clearly show that the R2 for NNETAR (0.885) have better value compared to ANN (0.695) even though the relationship between discharge and sediment concentration was weak, it outperforms the ANN. While based on the sediment rating curve (SRC) results, the same pattern was exhibited where the R2 for NNETAR show a greater value than ANN and SRC with R2 values of 0.695 and 0.451, respectively. Based on the observed results of quantified riverbank erosion, the most active transect eroded 1.747 mm/yr- while 0.657 mm/yr- is the least eroded. furthermore, the result reveals the maximum and minimum sediment contribution to the fluvial system from riverbank eroded to be 0.00743 tonnes/yr and 0.00148 tonnes/yr respectively. Lastly, by using discharge and percentage soil composition (sand and clay), a RbEI was developed by the adopted Equation 4.7 to estimate the status of riverbank erosion of River Kahang. Moreover, five classifications of erosion status were proposed, which can be used to describe the status and severity of the riverbank erosion. In conclusion, the estimates by the RbEI is expected to serve as basis for analysing and adopting river stabilisation and restoration design, which will be of importance to dam operators in making informed decisions regarding early warnings on the riverbank stability. Also, reliable sediment concentration estimation will assist in the development of catchment sediment budget which will give an insight into the effect of situating a dam on a river in terms of sediment supply and riverbank erosio

Automated Vision-Based Beverage Bottle Quality And Level Inspection System

Automated vision inspection emerged as an important part of the product quality monitoring process.It is a requirement of International Organization for Standardization (ISO) 9001 to appease the customer satisfaction in terms of frequent improvement of the quality of products.It is totally impractical to rely on human inspector to handle a large scale quality control production because human has major drawback in their performance such as inconsistency and time consuming. Therefore,an automatic inspection is a promising approach to maintain product quality as well as to resolve the existing problems relate to delay outputs and cost burden. This research presents a computerized analysis to detect defects occur in beverages production in order to minimize the defective products.Image processing techniques are proposed to detect defects of beverages bottle.The defects are categorized into three classes which are bottle shape defect, color concentration defect and liquid level defect.For shape defect detection,three techniques are proposed namely local standard deviation (LSD),morphological operation and adaptive thresholding. Statistical histogram,gray level co-occurrence matrix (GLCM) and quadratic distance are applied for color concentration defect detection. The liquid level is detected using Hough transform and coordinate of point techniques. The classification process is analyzed using rule-based and decision tree classifiers.In developing automated beverage bottle quality and level inspection system, the performance is verified in terms of accuracy.The simulation result demonstrate LSD,statistical histogram and Hough transform are selected as the best technique by achieving 98% of shape,93% of color concentration and 91% of liquid level. For the system result,93% average accuracy has achieved for three defect detections. The system is ready for internet of things (IoT) platform which is using raspberry pi that gives benefit to user for wirelessly access and monitor the results.For the results validation,field testing is conducted,and the proposed system shows the capability to classify the bottle defect accurately.Thus,it has proven the proposed system is appropriate to be implemented in real-time application for beverage bottle quality inspection

Automated Quality Control in Manufacturing Production Lines: A Robust Technique to Perform Product Quality Inspection

Quality control (QC) in manufacturing processes is critical to ensuring consumers receive products with proper functionality and reliability. Faulty products can lead to additional costs for the manufacturer and damage trust in a brand. A growing trend in QC is the use of machine vision (MV) systems because of their noncontact inspection, high repeatability, and efficiency. This thesis presents a robust MV system developed to perform comparative dimensional inspection on diversely shaped samples. Perimeter, area, rectangularity, and circularity are determined in the dimensional inspection algorithm for a base item and test items. A score determined with the four obtained parameter values provides the likeness between the base item and a test item. Additionally, a surface defect inspection is offered capable of identifying scratches, dents, and markings. The dimensional and surface inspections are used in a QC industrial case study. The case study examines the existing QC system for an electric motor manufacturer and proposes the developed QC system to increase product inspection count and efficiency while maintaining accuracy and reliability. Finally, the QC system is integrated in a simulated product inspection line consisting of a robotic arm and conveyor belts. The simulated product inspection line could identify the correct defect in all tested items and demonstrated the system’s automation capabilities

Chinese workers exploited by U.S.-owned iPhone supplier: An investigation of labor conditions at Jabil Green Point in Wuxi, China

This document is part of a digital collection provided by the Martin P. Catherwood Library, ILR School, Cornell University, pertaining to the effects of globalization on the workplace worldwide. Special emphasis is placed on labor rights, working conditions, labor market changes, and union organizing.CLW_2013_Report_Chinese_workers_exploited_by_US_iPhone_supplier.pdf: 568 downloads, before Oct. 1, 2020

Multi-Device Nutrition Control

Precision nutrition is a popular eHealth topic among several groups, such as athletes, 1 people with dementia, rare diseases, diabetes, and overweight. Its implementation demands tight 2 nutrition control, starting with nutritionists who build up food plans for specific groups or individuals. 3 Each person then follows the food plan by preparing meals and logging all food and water intake. 4 However, the discipline demanded to follow food plans and log food intake turns out into high 5 dropout rates. This article presents the concepts, requirements, and architecture of a solution that 6 assists the nutritionist in building up and revising food plans and the user following them. It does 7 so by minimizing human-computer interaction by integrating the nutritionist and user systems 8 and introducing off-the-shelf IoT devices in the system, such as temperature sensors, smartwatches, 9 smartphones, and smart bottles. An interaction time analysis using the Keystroke Level Model 10 provides a baseline for comparison in future work addressing both the use of machine learning and 11 IoT devices to reduce the interaction effort of users.info:eu-repo/semantics/publishedVersio

A Review Of Vision Based Defect Detection Using Image Processing Techniques For Beverage Manufacturing Industry

Vision based quality inspection emerged as a prime candidate in beverage manufacturing industry. It functions to control the product quality for the large scale industries; not only to save time, cost and labour, but also to secure a competitive advantage. It is a requirement of International Organization for Standardization (ISO) 9001, to appease the customer satisfaction in term of frequent improvement of the quality of products and services. It is totally impractical to rely on human inspector to handle a large scale quality control production because human has major drawback in their performance such as inconsistency and time consuming. This article reviews defect detection using image processing techniques for beverage manufacturing industry. There are comparative studies on techniques suggested by previous researchers. This review focuses on shape defect detection, color concentration inspection and level of liquid products measurement in a container. Shape, color and level defects are the main concern for bottle inspection in beverage manufacturing industry. The development of practical testing and the services performance are also discussed in this paper