Planning Usage Research Articles

Application of Different Sensors in Obstacle Avoidance of Delivery Robots

Application of Different Sensors in Obstacle Avoidance of Delivery Robots

An Analysis of Visitor Behaviors and Satisfaction in Urban Forests: Focused on Major Forests in Incheon

Background and objective: Urban forests have increasingly integrated recreational and welfare facilities to enhance the quality of life for urban residents. This study explores the behaviors, perceptions, and satisfaction of visitors to major forests in Incheon Metropolitan City, aiming to offer guidelines for the improvement of recreational facilities within these urban forests. By understanding visitor needs and preferences, the study seeks to contribute to the development of more user-friendly and sustainable forest environments that cater to the well-being of city dwellers.Methods: The study focused on five major forests in Incheon: Munhaksan Mountain, Cheongnyangsan Mountain, Manwolsan Mountain, Wonjeoksan Mountain, and Gyeyangsan Mountain. Data were collected through field surveys and analyzed using cross-tabulation, one-way ANOVA, and regression analysis. Based on the results, targeted recommendations were made for the enhancement of recreational facilities in each forest.Results: For Munhaksan Mountain, expanding recreational spaces and offering trail-specific programs are key priorities, along with developing a maintenance plan to improve visitor satisfaction. In Cheongnyangsan Mountain, expanding popular facilities like barefoot walking paths and optimizing the forest structure for air purification and noise reduction are needed. Manwolsan Mountain requires a more efficient maintenance system, focusing on security, amenities, and visitor services to address the issue of low satisfaction. Wonjeoksan Mountain should enhance trail safety with more CCTVs and improve amenities such as restrooms and trash disposal. Gyeyangsan Mountain needs more rest areas and amenities, addressing congestion in the northwest while preserving natural scenery and providing an information platform for younger visitors.Conclusion: This study contributes to optimizing the use of urban forests and improving the quality of life for residents by proposing a strategic framework for the development of recreational welfare in forests. The findings are expected to serve as a valuable resource for local governments in formulating rational forest management and usage plans.

Implementasi Augmented Reality sebagai Media Informasi Jadwal Penggunaan Ruang Laboratorium di Universitas Teknologi Yogyakarta

Universitas Teknologi Yogyakarta (UTY) operates several computer laboratory rooms used daily by students according to their scheduled times. Sometimes, these rooms experience sudden schedule changes, causing conflicts with other room bookings. This research aims to employ an Augmented Reality (AR)-based application for monitoring the dynamic schedule of lab room usage. The method utilizes a marker-based AR application, tested through black box implementation. The application successfully reads markers at distances of 10–90 cm, tilt angles of 10–80 degrees, and light intensities ranging from 40 to 250 lux. Under these conditions, the markers are readable, and the application responds by displaying virtual objects. Users of the lab rooms can utilize this AR application to identify changes in the scheduled usage plans at UTY as they occur.

Assessment and Prediction of Hydrometeorological Drought in Corong River Basin, Indonesia

Hydrological drought analysis in a river basin is crucial because it impacts water resource management, agriculture, drinking water supply, industry, ecology, and disaster risk mitigation. It plays a key role in water usage planning, safeguarding agricultural yields, and ensuring a stable drinking water supply. In the context of this research, the Corong River basin is used as a case study. This study aims to determine the level of hydrological drought (deficit) using the Threshold Level Method (TLM) and predict hydrological drought using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. This model understands the characteristics of drought and predicts drought in the Corong River basin for early mitigation and anticipation of drought in the region. The results of this research indicate a strong relationship between the Hydrological Drought Index (HDI) and streamflow and Precipitation (PRCP), which can be used to forecast future droughts. This model is highly robust and accurate in observing the level of hydrological drought (deficit, duration, and sharpness) and predicting drought events in the Corong River basin. These findings have significant practical implications for water management and disaster risk mitigation in this river basin while also advancing the scientific understanding of hydrological drought.

Searchable encryption algorithm in computer big data processing application

With the continuous development of computer technology, the amount of data has increased sharply, which has promoted more and more diversified data transportation and processing methods. At the same time, computer data analysis technology can effectively process data. This is reflected in the computer big data analysis technology not only can realize data visualization analysis, but also has data prediction and data quality management. The development of cloud computing network technology can not only provide convenience points for individuals, but also provide space for enterprises to store data. The emergence of keyword search encryption algorithms solves this problem. When users use keywords to search encryption algorithms, they can search for cipher text keywords to find the files or data they want in the cloud environment. At present, it has been widely used. In addition, this article also improves the keyword search plan and the user's query plan according to the dynamic changes of keywords, and proposes a user's multi-dynamic keyword search encryption plan. Through this program, users can search for encrypted files by keywords and change them, and the changed data will be dynamically updated. In this way, the program can realize multi-user data sharing, and can realize efficient search and dynamics.

Service-quality based pricing approach for charging electric vehicles in smart energy communities

Guiding electric vehicles (EVs) in smart energy communities to participate in demand response can significantly benefit users and the load aggregator. However, when the user's travel plans are uncertain, the EV may end up charging before the planned time, leaving the user with an insufficient power supply. This can occur because the control program chooses to charge the EV during periods of low electricity prices to reduce user costs. To address this issue, a new strategy for charging pricing based on service-quality is proposed to help users deal with the uncertainty of travel plans and promote user participation in demand response. In this paper, the proposed charging pricing of EVs consists of three parts. Firstly, the model of charging packages with different service qualities is designed for users. The differences in charging packages are reflected in the percentage of power supply and price within each time slot. Charging packages provide users with at least a certain amount of power in each time slot, allowing them to better cope with the uncertainty of their travel plans. Secondly, a subscription model for charging packages is built to match the user's travel plans. Finally, the pricing model of the charging package is established, representing the master-slave game between the load aggregator and the user. The model is linearized by the KKT condition. The results show that the proposed strategy helps users cope with the uncertainty of their travel plans, reducing charging cost for users by 16.39% while increasing revenue for the load aggregator by 14.8%.

A Transferable Prediction Approach for the Remaining Useful Life of Lithium-Ion Batteries Based on Small Samples

Predicting the remaining useful life (RUL) of batteries can help users optimize battery management strategies for better usage planning. However, the RUL prediction accuracy of lithium-ion batteries will face challenges due to fewer data samples available for the new type of battery. This paper proposed a transferable prediction approach for the RUL of lithium-ion batteries based on small samples to reduce time in preparing battery aging data and improve prediction accuracy. This approach, based on improvements from the adaptive boosting algorithm, is called regression tree transfer adaptive boosting (RT-TrAdaBoost). It combines the advantages of ensemble learning and transfer learning and achieves high computational efficiency. The RT-TrAdaBoost approach takes the charging voltage and temperature curve as input and utilizes the classification and regression tree (CART) as the base learner, which has better feature capture ability. In the experiment, the working condition migration experiment and battery type migration experiment are conducted on non-overlapping datasets. The verified results revealed that the RT-TrAdaBoost approach could transfer not only the battery aging knowledge between various working conditions but also realize the RUL migration prediction from lithium iron phosphate battery to lithium cobalt oxide battery. The analysis of error and computation time demonstrates the proposed method’s high efficiency and speed.

Spatiotemporal Analysis and Trend Detection of Groundwater Levels Using Gis Techniques in Nadia District of West Bengal, India

Abstract Groundwater (GW) resources are influenced by several factors like over exploitation, geological formation, climate etc. A long-term GW level trend detection is necessary for sustainable groundwater usage planning in future. In the present study, long-term (1990-2013) spatio-temporal analysis of GW levels during pre-monsoon and post-monsoon was done to elucidate the GW level variation trends in Nadia. Results show that the groundwater levels dropped more than 2.5 m below ground level (bgl) in the central part of the district, however, this drop was less than 2.5 m (bgl) in the northern and southern part of the district. According to pre- and post-monsoon data, the average annual water level decreased beyond 3 m (bgl) in blocks of Tehatta II, Chapra and Chakdah. Average GW level at Krishnaganj and Chakdah, in pre-monsoon were 6.84 m (bgl) and 7.55 m (bgl) respectively, which was higher than the post-monsoon (4.20 m (bgl) and 4.43 m (bgl)). During post-monsoon except Karimpur-I, Krisnaganj (both negative trend) and Hashkali (no trend) and in pre-monsoon except Krisnaganj (negative trend) and Hashkali (no trend), all the other blocks had positive trend. The study reveals that the groundwater levels in the southeastern and northeastern regions of the district drops by more than 5 m bgl in pre-monsoon and more than 4 m bgl in post-monsoon. Such fluctuations of groundwater levels may have serious bearing on agricultural operations in this intensively cultivated and irrigated area.

IMPACT OF PROJECT PLANNING PRACTICES ON THE PERFORMANCE OF CONSTRUCTION FIRMS IN UGANDA: A CASE STUDY OF MASAKA CITY, UGANDA

Construction is a complex activity as it contains several inputs to achieve the desired objective. Efficient project plan practices have a significant impact on the performance of construction companies. Several companies engage in construction activity with little or no planning. This study investigated project planning practices among the construction firms in Masaka City, Uganda with a view to understanding their performances. Qualitative and quantitative techniques in a cross-sectional approach were used in this study. Data was collected from professionals working in 20 construction firms in the city making a total of 175 participants. Using questionnaires and interview guides. The data collected were analyzed using descriptive and inferential statistics with the aid of SPSS software. Three project planning practices were considered: Safety and Resource Planning (SRP), Equipment and Material Usage Planning (EMU), and Work Scheduled and Time Planning. The results showed that none of the companies fully complied with the requirement planning practices, which led to material wastage and delays in project delivery. The regression model formulated has a coefficient of determination of 77.45%, indicating that the model has the capacity to predict about 78% of the performances of construction firms. The study concluded that the poor performance of the firms is influenced by the lack of planning practices. It is therefore recommended that strict conformance and enforcement of the project management principles throughout the lifecycle of the project should be given more attention.

Long-Term Observations of Atmospheric Constituents at the First Ground-Based High-Resolution Fourier-Transform Spectrometry Observation Station in China

Long-term observations of the volume mixing ratio (VMR) profiles and total columns of key atmospheric constituents are significant for understanding climate change and the impact of the carbon budget in China. This study provides an overview of the first ground-based high-resolution Fourier-transform spectrometry (FTS) observation station in China, which is located in Hefei, Eastern China. The FTS observation station can observe the total columns and VMR profiles of more than 30 atmospheric constituents. Time series of some key atmospheric constituents observed at the Hefei station since 2014 have been released to the public. The major scientific achievements obtained to date at this station include spectral retrieval characterization and harmonization, investigation of the overall characteristics of key atmospheric constituents, emission estimates, satellite and chemical transport model (CTM) evaluations, and a summary of pollutant sources and transport patterns. An outlook is also presented of the envisaged plan for observations, scientific studies, and data usage at the Hefei station. China has explicitly proposed reaching a peak in its CO2 emissions by 2030 and realizing carbon neutrality by 2060. The Hefei station will provide scientific assistance to the Chinese Government for developing green economy policies and achieving carbon neutrality and the goals of the Paris Agreement.

Water governance and food security of the irrigators association in Bago City, Negros Occidental, Philippines

Humans and all living organisms rely on water for survival. However, this dependence on water resources coupled with population increase, economic development, and accumulated disaster risk could lead to global water scarcity. With the pressing need to develop water governance roadmaps, this study presents relevant information for developing a plan for water usage, conservation, and recycling. Using a researcher-made survey instrument, a total of 124 residents from barangays Mailum and Taloc in Bago City, Negros Occidental, Philippines served as respondents. Results found that most of the respondents were aware of the Irrigation Association's (IA) policies on water governance and that they were appropriately designed and beneficial to their needs. Although water is available on their farm for irrigation, there were times when they experience water shortage, especially during the dry season. Moreover, even though irrigation water is properly used in the locality, water wastage and usage for other purposes were observed. In terms of irrigation for rice farming, availability ranged from always (49.2%) to sometimes (47.6%) and due to the lack of other bodies of water in their area, the majority claimed that they have no other sources of water aside from the irrigation system. The majority of the respondents also claimed that their rice production is sufficient for the rice needs of their family and their income is enough to cover their family's food expenditure. Lastly, four variables were found to have significantly contributed to the total rice production. These variables are (1) availability of water at all times, (2) equal allocation of water irrigation by the IA, (3) the power delegations of the IA to the members on the governance of irrigation water, and (4) the period when irrigation water was not distributed in the rice farm.

Performance Ratio Evaluation of Rooftop Solar Power Plant 32 KWp On Grid, Study Case in PT. KPJB Office Jepara Indonesia

In connection with Republic of Indonesia government regulation No. 79 of 2014 that mentioned 23% portion of Renewable Energy Usage Plan in 2025, PT KPJB as Operation and Maintenance of Tanjung Jati B Unit 3&4 CFPP has been implementing Solar Power Plant with capacity 32 KWp. It is used to supply daily office load likes computer, air conditioner, water pump, dispenser and lighting. However the electricity energy that produced is still not optimal yet due to weather condition. This is shown by the performance ratio calculation which is only worth 74.67% in April 2022. The power production average is 2,511.69 KWh per month, this is so far from total capacity installed and not sufficient for daily electricity energy used at the KPJB office.

Water Demand Prediction Using Machine Learning

bstract: Water is of paramount importance for the existence of life on Earth. The causes of water depletion are both natural and anthropogenic. On Earth, the amount of freshwater has remained persistent over span but the population has mushroomed. Therefore, striving for freshwater intensifies day by day. Proper management and forecasting are required for better and effective water usage plans. Water demand and population forecasting are the major parameters for an Urban Water Management. Machine learning is among the best-known techniques for such forecasting. Machine learning is a data analytics technique that provides machines the potential to learn without being comprehensively programmed. Unlike the traditional methods of demand forecasting that were not suitable for historical unstructured and semi structured data, machine learning takes into account or has the capabilities for analyzing such data.

Optimal real-time pricing and electricity package by retail electric providers based on social learning

As the global retail electricity market liberalizes, retail electric providers (REPs) are optimizing pricing schemes and electricity packages (EPs) to meet consumers' individual and diverse electricity plans while reducing volatility risk and ensuring profitability. However, consumer social learning (SL) helps frequently adjust electricity plans as social media evolves, preventing REPs from accurately understanding consumer electricity demand. Therefore, this paper constructs a dynamic game model of the retail electricity market considering social learning. REPs announce price and electricity package information to consumers based on market information and historical experience, and consumers first shared electricity usage plans based on their willingness to learn and share information among themselves, and then respond to REPs based on current information and experience. The main findings of this paper are as follows: (i) social learning increases consumer utility, promotes competition, depresses REPs prices, shrinks price differentials, and induces concessions by REPs, but raises volatility risk. (ii) electricity packages combined with social learning can save electricity consumption and significantly reduce volatility risk. Finally, this paper uses Smart Meter Energy Consumption data in London households to demonstrate the impact of social learning and to provide decision support for REP's electricity sales strategies.

Topology of Project Management Office in United Arab Emirates Project-Based Organizations

Majority of projects failed due to mismanagement factors such as funds, planning, time, and resources. With the adoption of Project Management Office (PMO) in an organization, it able to manage the projects practises through a distinctive office dedicated to project management with specific roles and functions that should be followed by the project officer for all the projects. The positioning of PMO in the organisational structure and its job responsibilities, are critical factors in project success. This paper presents a study to uncover the topology of PMO in the project-based organizations in United Arab Emirates (UAE). This qualitative study was conducted through interviews with twenty professionals working in PMO from four companies in the UAE. Based on the findings from the interview sessions, PMOs can be divided into three types: supportive, controlling, and directive. The result indicates that the type of PMO chosen have a significant impact on the efficiency of the project delivery. While controlling PMO approach, it is desired to "clear-house" the activities, processes, procedures, documentation, and more, the organisation requires that the help be used in addition to providing it, supportive PMO is the best choice if the goal is to establish a "rein in" of project management data that all project managers within the firm may freely access. Furthermore, Directive PMO goes beyond simple control and "takes over" projects by providing the necessary resources and project management expertise. According to the findings of the interviews, the PMO performs a variety of functions, including project support, training, lesson-learned maintenance, project management tool and software support, project control, inter-project communication, managing shared resources, and encouraging good project management and decision-making. PMO forecasts future resource requirements, plans for strategic, non-billable, and billable usage, and assigns appropriate resources to appropriate projects at appropriate times. This improves resource utilisation efficiency, and significantly lowers project resource costs.

Long Term Trend Analysis of River Flow and Climate in Northern Canada

Changes in water resources within basins can significantly impact ecosystems, agriculture, and biodiversity, among others. Basins in northern Canada have a cold climate, and the recent changes in climate can have a profound impact on water resources in these basins. Therefore, it is crucial to study long term trends in water flow as well as their influential factors, such as temperature and precipitation. This study focused on analyzing long term trends in water flow across the Athabasca River Basin (ARB) and Peace River Basin (PRB). Long term trends in temperature and precipitation within these basins were also studied. Water flow data from 18 hydrometric stations provided by Water Survey of Canada were analyzed using the Mann-Kendall test and Sen’s slope. In addition, hybrid climate data provided by Alberta Environment and Parks at approximately 10 km spatial resolution were analyzed for the ARB and its surrounding regions during 1950–2019. Trend analysis was performed on the water flow data on monthly, seasonal, and annual scales, and the results were cross-checked with trends in temperature and precipitation and land use and land cover data. The overall temperature across the basins has been increasing since 1950, while precipitation showed an insignificant decrease during this period. Winter water flow in the upper ARB has been slowly and steadily increasing since 1956 because of the rising temperatures and the subsequent slow melting of snowpacks/glaciers. The warm season flows in the middle and lower subregions declined up to 1981, then started to show an increasing trend. The middle and lower ARB exhibited a rapid increase in warm-season water flow since 2015. A similar trend change was also observed in the PRB. The gradual increase in water flow observed in the recent decades may continue by the mid-century, which is beneficial for agriculture, forestry, fishery, and industry. However, climate and land cover changes may alter the trend of water flow in the future; therefore, it is important to have a proper management plan for water usage in the next decades.

A Review of Deep Learning Techniques for Encrypted Traffic Classification

Network traffic classification is significant for task such as Quality of Services (QoS) provisioning, resource usage planning, pricing as well as in the context of security such as in Intrusion detection systems. The field has received considerable attention in the industry as well as research communities where approaches such as Port based, Deep packet Inspection (DPI), and Classical machine learning techniques were thoroughly studied. However, the emergence of new applications and encryption protocols as a result of continuous transformation of Internet has led to the rise of new challenges. Recently, researchers have employed deep learning techniques in the domain of network traffic classification in order to leverage the inherent advantages offered by deep learning models such as the ability to capture complex pattern as well as automatic feature learning. This paper reviews deep learning based encrypted traffic classification techniques, as well as highlights the current research gap in the literature. Index Terms : Traffic classification, Encrypted traffic, Deep learning, Machine learning.

Estimating charging demand from the perspective of choice behavior: A framework combining rule-based algorithm and hybrid choice model

It is of great significance to study the generation and spatio-temporal variation of electric vehicle charging demand for guiding the planning, construction and operation of charging facilities. Based on users' daily travel laws and behaviors, this paper proposed a framework for charging demand estimation, which combines rule-based algorithm and discrete choice model. The generation process of charging demand is divided into two choice stages. Stage one is before travel, where users choose whether to charge in the trip chain. Stage two is during travel, where users choose the charging location. The hybrid choice model based on the mixed logit model is constructed for the two choice stages respectively. This model can comprehensively consider the influence of several factors on charging choice behavior, including individual attitudes, preference heterogeneity, electric vehicle status, traffic attributes, parking place attributes and charging service level. In addition, the rule-based algorithm can establish the correlation within a trip chain, which refers to influence of the previous episode on the current episode. It can also update the status changes of battery electric vehicles and trips. In this way, the spatio-temporal coupling estimation of charging demand is achieved. Next, two stated preference surveys are conducted through online platform and field survey. The estimation results show that both probabilistic models have achieved high goodness-of-fit, and the proposed model can both characterize the influence of psychological attitude and preference heterogeneity of electric vehicle users on choice behavior. The parameter calibration results show that the user's travel plan plays a very important role in the process of generating charging demand. Besides, the attributes of the parking place and the service quality of the charging facilities are important factors affecting the choice of the charging location. The research results can guide the formulation of charging facility planning and operation strategies.

Techno-economic assessment of hydrogen production via steam reforming of palm oil mill effluent

Malaysia is one of the most prosperous palm oil producing countries, the palm oil mill effluent (POME) created during the process poses a high threat due to its high pollution characteristics. Besides, most palm oil mills in Malaysia treat POME using open ponding systems which cause greenhouse gas dissipation. On the other hand, there is a rising consciousness globally to use clean and renewable fuels. Malaysia is also having plans for clean hydrogen energy usage. However, the deployment of hydrogen technology in Malaysia is not meeting the targets set. One of the reasons is due to the high deployment cost of hydrogen technology. If POME can be reused for hydrogen production, it might help to minimize the waste generated from the palm oil industry while serving as a cheaper feedstock for hydrogen production. Before hydrogen production from POME can be successfully implemented, knowing its production cost is essential. Therefore, this study aims to elucidate the process design and techno-economic feasibility of hydrogen production from POME via a steam methane reforming technique in the context of Malaysia, which is currently limited in the literature. The process modelling part of the study was carried out using Aspen Plus software. The simulation results showed that a feedstock of 20,000 kg per hour of POME could generate 963.31 tonnes of hydrogen (purity of 99.9 %) annually. The hydrogen production facility was estimated to have total capital expenses and total operating expenses of RM 142.49 million and RM 58.67 million/year, respectively. The results also revealed that the production profitability was viable at a payback period of 8.5 years and a return of investment on year 20 of 18.48 %. The results from this study can be a reference for potential alternative hydrogen production methods. With the information on the economic viability, it is believed that it can help to accelerate the commercialisation of hydrogen production technologies. This study is also helpful for those who are trying to reduce the environmental impact of palm oil industries for a more sustainable future.

Anomaly detection in the context of long-term cloud resource usage planning

This paper describes a new approach to automatic long-term cloud resource usage planning with a novel hybrid anomaly detection mechanism. It analyzes existing anomaly detection solutions, possible improvements and the impact on the accuracy of resource usage planning. The proposed anomaly detection solution is an important part of the research, since it allows greater accuracy to be achieved in the long term. The proposed approach dynamically adjusts reservation plans in order to reduce the unnecessary load on resources and prevent the cloud from running out of them. The predictions are based on cloud analysis conducted using machine learning algorithms, which made it possible to reduce costs by about 50%. The solution was evaluated on real-life data from over 1700 virtual machines.