Double Counting Problem Research Articles

A Passenger Detection and Action Recognition System for Public Transport Vehicles

In the past few years, advances of autonomous driving technologies have made significant progress. One successful deployment with great potential is for the self-driving public transportation. Consequently, the safety of passengers under unmanned operations is an important issue for the future development. In this paper, we propose a vision system for the detection, counting and action recognition of passengers on board a bus. To have a complete coverage of the passenger area, a pair of cameras mounted on the ceiling is adopted for image acquisition. A new cross-camera CNN structure is proposed for passenger pose recognition and action classification. To deal with the double counting problem caused by the overlap of multiple viewpoints, a region prediction algorithm is developed for matching validation. The experiments carried out on a minibus have demonstrated the feasibility of our proposed techniques.

Shrimp Larvae Counting Based on Improved YOLOv5 Model with Regional Segmentation

Counting shrimp larvae is an essential part of shrimp farming. Due to their tiny size and high density, this task is exceedingly difficult. Thus, we introduce an algorithm for counting densely packed shrimp larvae utilizing an enhanced You Only Look Once version 5 (YOLOv5) model through a regional segmentation approach. First, the C2f and convolutional block attention modules are used to improve the capabilities of YOLOv5 in recognizing the small shrimp. Moreover, employing a regional segmentation technique can decrease the receptive field area, thereby enhancing the shrimp counter’s detection performance. Finally, a strategy for stitching and deduplication is implemented to tackle the problem of double counting across various segments. The findings from the experiments indicate that the suggested algorithm surpasses several other shrimp counting techniques in terms of accuracy. Notably, for high-density shrimp larvae in large quantities, this algorithm attained an accuracy exceeding 98%.

Allocation of carbon emission responsibility among Chinese cities guided by economic welfare gains: Case study based on multi-regional input-output analysis

Ensuring equitable allocation of carbon emission responsibilities at the city level is crucial for effectively mitigating climate change. However, traditional allocation methods like production-based accounting (PBA) and consumption-based accounting (CBA) encounter double-counting problems at this scale. This study for the first time addressed the ignored externality of carbon emissions among bilateral trading partners at city scale, achieving equitable carbon emission responsibility reallocations using the economic welfare gain-based (EWG) method. Examining 313 cities in China using a multi-regional input-output model, the highest carbon emission responsibilities are identified in Baotou at 400.7 Mt., followed by Yunnan (189.9 Mt) and Dalian (166.7 Mt). In this case, the EWG method reallocates producer and consumer responsibilities in bilateral trades. Among the 313 cities, emission responsibilities from 198 are situated between PBA and CBA, avoiding one-sided assignment of emissions based solely on either principle. A comparative analysis of allocation methods in China further indicates a predominance of production responsibility, mirroring a similar trend between EWG and PBA; a significant portion of producer responsibilities are identified within EWG. The results of this work may offer a fairer and economically sound basis for allocating emission responsibilities among cities, thereby facilitating more effective emission reduction strategies.

Shared Trading Strategy of Multiple Microgrids Considering Joint Carbon and Green Certificate Mechanism

With a background of carbon peak and neutrality, the economic and environmental requirements are increasing for microgrids. In view of the problem of energy wastage and conflicts of interest among multiple microgrid integrated energy systems, it is important to study the operation optimization of microgrid clusters while considering the sharing and trading of both carbon emissions and green certificates. In this study, a Stackelberg game mechanism is applied, in which the microgrid operator is the leader and its subscribers are the followers, forming a master–slave interaction model. Following this, breaking the trading barriers of energy and various policy markets, the joint carbon and green certificate trading mechanism is proposed. Moreover, a mutually beneficial shared trading model of multi-microgrids considering coupled energy and carbon and green certificate trading is proposed to avoid the problem of double counting of environmental attributes. In addition, a cooperative sharing center is assumed to propose a flexible multi-resource sharing price mechanism. It guides each microgrid operator to conduct internal multi-resource sharing trading, so as to reduce the daily operating costs of energy supplying entities in the cooperative system of multiple microgrids, effectively reduce carbon emissions, and improve the balance of network group mutual aid. According to the simulation results of an illustrative example, the proposed trading strategy can effectively unlock the potential of resource sharing and mutual aid within multi-microgrids and improve the economy and carbon reduction effects of the overall system.

Effective way to measure the specialization levels of an economy in the era of global value chains

• Proposal for a new index of revealed comparative advantages, ideal for global value chains, since it is on a linear scale and without the traditional double counting. • Analysis of the changes between the new index of revealed comparative advantages and the previous one, referring to the computer and electronics industry. • Creating a balance between the new index of revealed comparative advantages and the new index of revealed technological advantages. Traditionally, comparative advantages have been measured by the volume of gross exports and with formulas that give asymmetric results. However, with the globalization of value chains, other indices have emerged to partially solve some of these problems. The objective of this study is to create a new index for a linear comparison of different economies around the world at the sectoral specialization level, and without the problem of double international counting originated in the global value chains. The methodology used focuses on the properties of the new index and on the comparison with a previous index that only solved the problem of double counting. The results confirm that the new indicator is more reliable than the other index, especially in more advanced sectors of global value chains. Therefore, it should be used to establish policies related to international trade by different economies and global organizations.

Functional-Based Description of Electronic Dynamic and Strong Correlation: Old Issues and New Insights.

Approximate functionals in Kohn-Sham density functional theory (KS-DFT) and reduced density matrix functional theory (RDMFT) have advantages in dealing with dynamic correlation and strong correlation, respectively; their combination can benefit from complementarity while suffering from the problem of correlation double-counting. Herein, a short-range corrected reduced density matrix (1-RDM) functional is developed to take advantage of the functionals in KS-DFT and RDMFT without double-counting. The resulting functional, denoted as ωP22, outperforms other 1-RDM functionals for the tests of thermochemistry, nonbonded interactions, and bond dissociation energy. In particular, ωP22 shows much less systematic error for systems involving fractional spins, and it can properly predict the energies at both equilibrium and dissociated distances for different single and multiple bonds, which cannot be achieved by commonly used KS-DFT and RDMFT functionals. Therefore, ωP22 is demonstrated effective in balance handling dynamic and strong correlation, and the advances in this work would create new possibilities for the development and application of approximate functionals.

Deep-learning-based in-field citrus fruit detection and tracking.

Fruit yield estimation is crucial for establishing fruit harvest and marketing strategies. Recently, computer vision and deep learning techniques have been used to estimate citrus fruit yield and have exhibited notable fruit detection ability. However, computer-vision-based citrus fruit counting has two key limitations: inconsistent fruit detection accuracy and double-counting of the same fruit. Using oranges as the experimental material, this paper proposes a deep-learning-based orange counting algorithm using video sequences to help overcome these problems. The algorithm consists of two sub-algorithms, OrangeYolo for fruit detection and OrangeSort for fruit tracking. The OrangeYolo backbone network is partially based on the YOLOv3 algorithm, which has been improved upon to detect small objects (fruits) at multiple scales. The network structure was adjusted to detect small-scale targets while enabling multiscale target detection. A channel attention and spatial attention multiscale fusion module was introduced to fuse the semantic features of the deep network with the shallow textural detail features. OrangeYolo can achieve mean Average Precision (mAP) values of 0.957 in the citrus dataset, higher than the 0.905, 0.911, and 0.917 achieved with the YOLOv3, YOLOv4, and YOLOv5 algorithms. OrangeSort was designed to alleviate the double-counting problem associated with occluded fruits. A specific tracking region counting strategy and tracking algorithm based on motion displacement estimation were established. Six video sequences taken from two fields containing 22 trees were used as the validation dataset. The proposed method showed better performance (Mean Absolute Error (MAE) = 0.081, Standard Deviation (SD) = 0.08) than video-based manual counting and produced more accurate results than the existing standards Sort and DeepSort (MAE = 0.45 and 1.212; SD = 0.4741 and 1.3975).

PP254 Double-Counting In Evidence Synthesis Including Routinely Collected Data: Methodological and Practical Considerations

IntroductionThe use of real-world data, as an alternative to randomized controlled trials, is becoming increasingly common in the evaluation of new health technologies. With this rise in real-world literature, such data will also enter evidence synthesis models. While it can be beneficial to utilize data from all available sources, this can introduce the problem of double-counting of participants.MethodsUsing a number of case-studies, we discuss and illustrate various issues around double-counting. These include synthesis of studies using the same database or the same subset of participants, overlapping use of intervention arms across studies and the use of registry data from the participants overlapping with those in randomized controlled trials. The implications in research are considered along with common methods used currently to overcome these issues.ResultsDouble-counting of participants in evidence synthesis can artificially inflate precision, potentially leading to inappropriate conclusions. Common methods currently used to help mitigate the impact of double-counting includes stratifying analysis to different timelines, using the most comprehensive study in the evidence synthesis model or using the study that has the largest sample size. However, in all of these cases, sensitivity analyses would need to be considered to ensure robust results.ConclusionsCurrently, there are no published guidelines on how to address the issue of double-counting. With the increased use of real-world data in evidence synthesis, double-counting has the potential to become a significant issue. Therefore, it is of significant importance that methodologies and guidelines are developed to address this.

Global Value Chain Formation and Human Capital: Case of Korea and ASEAN

Purpose – This study discusses the effects of human capital in the formation of GVC linkages. We also investigate GVC intensity between Korea and ASEAN. Design/methodology – To solve the doubling-counting problem in evaluating comparative advantage, RCA has been re-computed using domestic value-added (henceforth RCA_VA) at the country-sector level instead of value of trade. The impact of human capital on GVC intensity was empirically analyzed by establishing a panel data set with four industries (ISIC Rev. 4) in eight ASEAN countries from 2005 to 2015 from OECD-TiVA and WDI. Findings – The empirical results show that human capital has a negative effect on GVC intensity in the agriculture and manufacture industries, while it has a positive effect in the service and information industries. The results do not mean that low human capital is a barrier and inefficient to GVC linkages. Low Value-added activities may be more profitable to some emerging countries. These findings suggest that it is important to accurately identify the competitive elements to increase gains from trade under the GVC. Also, it shows that comparative advantages can be misled by an RCA index evaluated in trade volume under the GVC. Originality/value – This study highlights the importance of human capital as a factor for the efficient formation of Global Value Chain (GVC). This study has different from the literature in analyzing the role of human capital in formation of linkage of the GVC. And we clarify the changing patterns of trade by removing the double-counting problem under the GVC.

Research on Particle Swarm Practical Algorithm of Partial Differential Exact Solution Based on Computer Software Analysis

Particle swarm collaboration has many applications on the Internet. It is necessary to cluster a large number of algebraic matrices to obtain enough data. The current research focuses on the correlation between particle swarm coordination and matrix, and does not fully consider the difference between geometries. There is a large overlap, which leads to the same results for different matrices, leading to double counting problems. Therefore, based on the analysis of computer technology, this paper proposes a particle swarm optimization algorithm based on partial differential exact solutions. The selected geometric matrix has higher correlation and lower overlap. Experimental results show that this method can significantly improve the accuracy and efficiency of particle swarm coordination.

Revisiting the Dutch disease thesis from the perspective of value-added trade

Soaring resource prices have again focused worldwide attention on the Dutch disease (DD) hypothesis, especially with regard to resource-exporting countries. However, recent decades’ growing intermediate trades have led to a double-counting problem, which makes conventional trade statistics inappropriate measures of the value of goods that any particular country trades, and subsequently distorts the DD effects. This paper provides an alternative measurement of DD symptoms based on global input-output tables and linked to the value-added trade concept. Since DD symptoms are associated with the macroeconomic circumstances that each country faces and the DD effects that implementing government management policies can, to some extent, counteract, we constructed a country-based composite index: the Dutch Disease Diagnosis Index (DDDI). We did so to more closely identify the effect that might have a higher likelihood of DD during specific times. We find that mineral-dependent countries exhibit several symptoms of DD, especially since mineral prices started rising rapidly after 2003. Brazil and Australia have the highest likelihood of suffering from DD.

The Principles and Practices of Case Classification and Counting: Lessons from the Civil and Criminal Jurisdictions of the Jamaican Court System

This study makes a seminal contribution to the evolution of court statistics as one of the first to propose a comprehensive set of case classification categories and associated theoretical and mathematical rules of case counting. The study is based on a three-year mixed-methods analysis into the criminal and civil case processes in the Jamaican court system. From this investigation, it proposes the use of six case status states - active, inactive, reopened, reactivated, pending/pre-judicial filings, and disposed. These classifications can be used to seamlessly organize cases and their constituent matters in a manner that avoids the common problems of miscounting, misclassification, and double counting of cases in many judiciaries around the world. The study further proposes the logic which may be applied in counting and arranging cases and their associated matters into the set of main, mutually exclusive classification categories of active, inactive, disposed, and pending/pre-judicial filings. Taken together, the findings of the study create the basis for court systems to collect and maintain reliable judicial statistics and to therefore make better decisions in planning and policy design and to more effectively marshal scarce resources.

The drivers of export value-added in China’s provinces: a multi-regional input–output model

ABSTRACT It is vital to calculate Chinese provinces’ exports in domestic and global value chains from the perspective of value-added to address the problem of double counting. In this study, we calculated the direct and indirect domestic value-added in exports of 30 Chinese provinces using cross-border and cross-regional input–output tables in 2007 and 2012. We analysed the drivers of export value-added based on structural decomposition analysis. The results show that there is a value-added spatial imbalance in trade development. The export value-added of eastern provinces is significantly higher than that of central and western provinces, but this imbalance is gradually improving. The domestic value chain is the main channel for each province to realize export value-added, and the province production channels account for the highest proportion in this chain. The export value-added through international production channels is relatively small but growing in importance. The structural analysis shows that the increase of Chinese provinces’ export value-added is mainly caused by an increase of export scale and the provincial multiplier effect, while the value-added ratio of Chinese provinces has the main restraining effect. The spillover effect from industrial cooperation between provinces and other economies also increases the domestic value-added of exports.

Double-counting problem of the bonus–malus system

The bonus–malus system (BMS) is a widely used premium adjustment mechanism based on policyholder’s claim history. Most auto insurance BMSs assume that policyholders in the same bonus–malus (BM) level share the same a posteriori risk adjustment. This system reflects the policyholder’s claim history in a relatively simple manner. However, the typical system follows a single BM scale and is known to suffer from the double-counting problem: policyholders in the high-risk classes in terms of a priori characteristics are penalized too severely (Taylor, 1997; Pitrebois et al., 2003). Thus, Pitrebois et al. (2003) proposed a new system with multiple BM scales based on the a priori characteristics. While this multiple-scale BMS removes the double-counting problem, it loses the prime benefit of simplicity. Alternatively, we argue that the double-counting problem can be viewed as an inefficiency of the optimization process. Furthermore, we show that the double-counting problem can be resolved by fully optimizing the BMS setting, but retaining the traditional BMS format.

The Impact of China-US Trade Conflict on Korean Economy

This paper concentrated on how US-China Trade conflicts would have impacts on Korean economy. Making the best use of WIOTs (World Input Output tables), this research focused on value added contents in trade instead of traditional trade statistics in the light of double-counting problem. Thus, the paper set two feasible bad-case scenarios: when China and US’s GDP fall and when China and US’s exports fall. Making a comparison of the results from the two models, the ripple effects that Korea would suffer from are bigger in China’s case. Even though it is under the same condition where China and US’s GDP identically fall by 1%, China’s case would bring about stronger impacts on Korea. In a nutshell, the outcome illustrates that the extent of the damage that Korea would undergo is always larger in China’s bad-case scenarios compared to US’s bad-case scenarios which reveals that Korea is more associated with China than US in global value chains. The findings of the analysis are arranged and diagrammed in a table as follows. Keywords: China, US, China-US Trade War, Korean Economy, WIOTs.

Economic contributions of wild fisheries and aquaculture: A social accounting matrix (SAM) analysis for Gyeong-Nam Province, Korea

This study quantifies and compares the economic contributions of two alternative methods of producing raw fish – wild fisheries and aquaculture – in Gyeong-Nam (GN) province, Korea. This study departs from most previous studies in several important ways. First, this study employs a social accounting matrix (SAM) model to overcome the inability of input-output (IO) models to address the distributional effects. Second, this study conducts a regional-level analysis rather than a national-level analysis. Third, this study uses an adjusted demand-driven SAM modeling framework to avoid double-counting problem with the conventional Leontief demand-driven model and the problem with Ghosh (1958) approach. Results indicate that the contributions per unit of output from the two different fish producing industries to total provincial employee compensation and total provincial business surplus are substantially different, but that the contributions (per unit of output) to total provincial output, total household income, and provincial government revenue are not significantly different between the two different fish producing industries. Results also reveal that the non-seafood industries that benefit most from wild fisheries are starkly different from those that gain most from aquaculture.

Physical layer authentication under intelligent spoofing in wireless sensor networks

Location based access in wireless sensor networks (WSN) are vulnerable to location spoofing attacks. In this paper, we investigate the physical layer (PHY-layer) authentication in the threat of an intelligent location spoofing attack. The intelligent attack can emulate the legitimate channel information and maximize its long-term cumulative reward. First, we analyze the feasibility of this intelligent attack and investigate how it threats to the networks. Specifically, we derive the optimal transmit power allocation and find the worst case for the defenders, namely optimal intelligent attack, in which the attacker can learn the intelligent attack action based on the beamforming with optimal transmit power allocation. To defend against such an intelligent attack with high accuracy and low overhead, we develop a cooperative PHY-layer authentication scheme. Then, we provide an in-depth analysis on the belief and derive the belief bounds and the closed-form expression for the belief threshold. Furthermore, considering the whole computation complexity and the double counting problem in a loopy graph, we propose the cooperative neighbour selection algorithm to accelerate belief convergence and reduce the overhead. Finally, the simulation results reveal that the proposed method can significantly improve the defense performance compared with the state-of-art methods.

Similarity Analysis of Exports Value Added by Country and Implication for Korea’s Global Value Added Chains

Purpose - This paper investigates the structure of exports across countries in terms of value added. Exports value added is examined under two categories, domestic and overseas. Using a statistical classification method by distance based on these two value added categories, this paper estimates the similarity of exports value added across countries including Korea. Design/methodology - The model of study is to employ a generalized distance function and then derive the Manhattan and Euclidean distances. The paper also performs cluster analysis using the Partitioning Around Medoids (PAM) and hierarchical methods to classify the 44 sample countries considered in this study. Findings - Our main findings are as follows. The 44 countries can be classified under 5 groups by their domestic and overseas value added in exports. Korea has a sandwich global value chains (GVCs) position between Japan, China, and Taiwan in the East Asian region. Originality/value - Existing papers point out the double counting problem of trade statistics as the intermediate goods trade across borders increases. This paper addresses the double counting problem by using the World Input-Output Table. The paper shows the need to explore the similarity of value added in exports structure across countries and investigate the GVCs position and role of each country.

The Costs of Reduction Emission from Deforestation and Forest Degradation. Concepts and Issues

This study examines the concept of “Reduction Emission from Deforestation and Forest Degradation” - REDD costs by using the existence costs of Boucher (2008) such as opportunity costs, transaction costs, implementation costs, stabilization costs, and administration costs. In addition, it provides some actual issues of REDD. The study proves that global simulation models yield far higher REDD prices than empirical models, however, they can be criticized for their use of aggregated data and other simplifications. Moreover, adding implementation, administration, stabilization costs into transaction costs could potentially lead to double-counting problem. Finally, REDD is an inexpensive option compared to reducing emissions in the energy sectors of industrialized countries and has a potential to generate substantial benefits in addition to the reduction of greenhouse gas emissions. Yet, it is important for countries to manage these issues that can be potentially defect REDD adoption.

Correlation matrix renormalization theory for correlated-electron materials with application to the crystalline phases of atomic hydrogen

Developing accurate and computationally efficient methods to calculate the electronic structure and total energy of correlated-electron materials has been a very challenging task in condensed matter physics and materials science. Recently, we have developed a correlation matrix renormalization (CMR) method which does not assume any empirical Coulomb interaction $U$ parameters and does not have double counting problems in the ground-state total energy calculation. The CMR method has been demonstrated to be accurate in describing both the bonding and bond breaking behaviors of molecules. In this study, we extend the CMR method to the treatment of electron correlations in periodic solid systems. Using a linear hydrogen chain as a benchmark system, we show that the results from the CMR method compare very well with those obtained recently by accurate quantum Monte Carlo (QMC) calculations. We also study the equation of states of three-dimensional crystalline phases of atomic hydrogen. We show that the results from the CMR method agree much better with the available QMC data in comparison with those from density functional theory and Hartree-Fock calculations.