Issues of the Risk-Based Approach

The purpose of the article was to analyze the problems currently standing in the way of more effective application of the risk-based approach methodology in the field of technogenic safety management. Methods — theoretical, inductive method, analysis of own experience, adopted normative legal acts, other publications. The main results of the work include the following: • despite the broadest use of the concept of “risk” in the field of technogenic safety management, there is no generally accepted interpretation of it to date; • often the evaluative concept of “risk” is mistakenly used instead of objectively existing risk factors; • quantifiably technogenic risk should be characterized by indicators of numerical nature, having vector objects, since two components should be indicated for the complete assignment of the indicator: the probability and the amount of damage; • experience shows that the methods of assessing the probabilistic component of risk indicators recommended by regulatory documents on the analysis and quantification of technogenic risk are accompanied by a very large uncertainty, therefore, instead of the traditional point statement, a more adequate method of assessment is the use of an interval approach that takes into account and allows quantifying this uncertainty; • the analysis showed that the so-called frequency approach, which is most often used to assess the probabilistic component of technogenic risk indicators, is used improperly, has no basis, since, as a rule, the phenomenon of statistical stability is not observed in the object area of the technosphere, there are no general aggregates; • in society and even among specialists, by now there is no understanding of the need to express all three components of damage from an accident (explosion/fire) in monetary terms, without which it is impossible to estimate and express the amount of total damage: — in conclusion, four key problems that hinder the more effective use of the risk-based approach methodology in the field of technogenic safety management are listed; — imperfection of the existing methodological base for the analysis and quantitative assessment of technogenic risk; — the problem of staffing in the field of technogenic risk management; — lack of national criteria for acceptable risk; — complete disregard of the problem of uncertainty of the results of the COR, the lack of methodological support for the procedure of analysis and quantification of this uncertainty. Conclusion — the efforts of the entire community of specialists-researchers, legislators, practitioners engaged in various aspects of the problem of technosphere safety management are necessary to solve the tasks specified in the article.

Crop Agnostic Monitoring Driven by Deep Learning

Farmers require diverse and complex information to make agronomical decisions about crop management including intervention tasks. Generally, this information is gathered by farmers traversing their fields or glasshouses which is often a time consuming and potentially expensive process. In recent years, robotic platforms have gained significant traction due to advances in artificial intelligence. However, these platforms are usually tied to one setting (such as arable farmland), or algorithms are designed for a single platform. This creates a significant gap between available technology and farmer requirements. We propose a novel field agnostic monitoring technique that is able to operate on two different robots, in arable farmland or a glasshouse (horticultural setting). Instance segmentation forms the backbone of this approach from which object location and class, object area, and yield information can be obtained. In arable farmland, our segmentation network is able to estimate crop and weed at a species level and in a glasshouse we are able to estimate the sweet pepper and their ripeness. For yield information, we introduce a novel matching criterion that removes the pixel-wise constraints of previous versions. This approach is able to accurately estimate the number of fruit (sweet pepper) in a glasshouse with a normalized absolute error of 4.7% and an R2 of 0.901 with the visual ground truth. When applied to cluttered arable farmland scenes it improves on the prior approach by 50%. Finally, a qualitative analysis shows the validity of this agnostic monitoring algorithm by supplying decision enabling information to the farmer such as the impact of a low level weeding intervention scheme.

Penilaian Kawasan Perumahan dengan Pendekatan Konsep Berkelanjutan (Greenship Neighborhood Assessment)

Global warming and environmental degradation are still being issues in various countries, including Indonesia. It has been causing the climate change. In 2015 the United Nations started a program called Sustainable Development Goals (SDGs). One of the goals was to tackle climate change. In order to encourage the implementation of SDGs, an effort that could conduct was the realization of green housing areas. An assesment is needed to make sure that sustainable development in neighborhood is implemented well and correct. Green Building Council Indonesia (GBCI) has published a rating system called Greenship Neighborhood as a reference for assessing the concept of sustainable development neighbourhood. The purpose of this study was to find out how the implementation of Greenship Neighbourhood and how much value is achieved in Citraland residential area. The method that used were descriptive qualitative and quantitative. Descriptive qualitative was a description of the research object conditions, while descriptive quantitative was output assessments numbers based on the Greenship Neighborhood assessment tool. There were seven aspects that assessed i.e. land ecological enhancement, movement and connectivity, water management and conservation, solid and material waste, community wellbeing strategy, buildings and energy, and innovation future development. The results of research showed that there were six of the seven assessment categories have been applied in the object area. Generaly, the total value is 36 out of 124, with percentage achieved 29%. Based on these result, Citraland Pekanbaru residential area did not meet the minimum standard for the application of Greenship Neigborhood according to GBCI.

Analisis Perubahan Penutupan Lahan dan Potensi Karbon di Taman Hutan Raya Pocut Meurah Intan, Aceh Indonesia

Perubahan penutupan lahan merupakan sektor penyumbang emisi gas rumah kaca terbesar, termasuk di dalamnya adalah pemanfaatan lahan. Analisis tutupan lahan menjadi bagian penting dalam menentukan jumlah potensi karbon yang tersedia. Penelitian bertujuan untuk menganalisis perubahan tutupan lahan dari tahun 2003 hingga 2018 dan menghitung potensi karbon di Taman Hutan Raya Pocut Meurah Intan dengan luas objek penelitian 6.215 ha. Penelitian dilaksanakan selama 5 (lima) bulan. Penelitian ini menggunakan metode stock difference, yaitu metode perhitungan luas tutupan lahan dan stok karbon pada dua titik waktu. Hasil penelitian menunjukkan bahwa perubahan luas tertinggi tahun 2018 seluas 263 ha dan terendah tahun 2009 seluas 108 ha. Lahan terbuka meningkat seluas 100 ha, pemukiman 81 ha, semak belukar 65 ha, pertanian lahan kering campur semak 32 ha. Sementara hutan lahan kering sekunder menurun 79 ha, hutan tanaman 76 ha, savanna 21 ha dan pertanian lahan kering 103 ha. Selama kurun waktu 15 tahun berdasarkan kelas penutupan lahan, cadangan karbon tertinggi pada tahun 2003 sebesar 656.053 ton, terendah tahun 2012 sebesar 620.992 ton. Laju serapan karbon tertinggi pada periode tahun 2015-2018 sebesar 94.615 ton CO2 dan terendah pada periode tahun 2009-2012 sebesar 1.981 ton CO2. Laju emisi tertinggi pada periode tahun 2003-2006 sebesar 79.559 ton CO2 dan terendah periode tahun 2006-2009 sebesar 9.069 ton CO2. Peningkatan serapan karbon diakibatkan oleh meningkatnya luas tutupan lahan pada hutan lahan kering sekunder dan adanya pemanfaatan lahan untuk pertanian lahan kering campur semak.ABSTRACTChanges in land cover are the largest contributor to greenhouse gas emissions, including land use. Land cover analysis is an important part in determining the potential amount of carbon available. The study aims to analyze changes in land cover from 2003 to 2018 and calculating the carbon potential in the Pocut Meurah Intan Forest Park with a research object area of 6,215 ha. The research was conducted for 5 (five) months. This research uses the stock difference method, namely the method of calculating land cover area dan stok karbon pada dua titik waktu. The results showed that the highest area change in 2018 was 263 ha and the lowest was in 2009 at 108 ha. Open land increased by 100 ha, settlement 81 ha, scrub 65 ha, dry land agriculture mixed with shrubs 32 ha. Meanwhile, secondary dry land forest decreased by 79 ha, plantation forest 76 ha, savanna 21 ha and dry land agriculture 103 ha. Over a 15 year period based on land cover class, the highest carbon stock in 2003 was 656,053 tons, the lowest was in 2012 at 620,992 tons. The highest carbon absorption rate in the 2015-2018 period was 94,615 tons of CO2 and the lowest was in the 2009-2012 period of 1,981 tons of CO2. The highest emission rate in the 2003-2006 period was 79,559 tonnes of CO2 and the lowest for the 2006-2009 period was 9,069 tonnes of CO2. The increase in carbon sequestration is caused by the increase in land cover in secondary dryland forest and the use of land for mixed dry land agriculture.

PERAN STAKEHOLDERS DALAM PENGELOLAAN OBJEK WISATA ALAM SIWANG PARADISE DI DESA SIWANG KOTA AMBON

The potential of the natural tourist attraction of Siwang Paradise will have a high attractiveness value if it is supported by the role of stakeholders in management and development. The purpose of this study is to analyze the role of stakeholders in the management and development of the natural tourist attraction of Siwang Paradise, and what obstacles are experienced by stakeholders in optimally managing and developing these objects. The method used is descriptive qualitative, the determination of respondents is based on snowball sampling technique, the analysis is carried out inductively based on facts and the results of interviews with respondents and key actors. The results showed that the stakeholders involved in the management of the Siwang Paradise object were the key actors (area owners) and the main actors (surrounding communities). Constraints in the management of the Siwang object are due to the lack of trust within the area owner to involve supporting stakeholders, in this case the private sector, NGOs, and academics. So that the management of objects in developing various readiness of tourist attraction products becomes slow and runs not optimally. The visible impact of the management is the economic impact, through increasing family income, social impact, reducing unemployment, opening up road access, making motorcycle taxis easier to use, making it easier for people to do their activities. While the ecological impact is the management is carried out by paying attention to the cleanliness of the environment from waste, and based on the carrying capacity of the object area.

Semantic-Guided Attention Refinement Network for Salient Object Detection in Optical Remote Sensing Images

Although remarkable progress has been made in salient object detection (SOD) in natural scene images (NSI), the SOD of optical remote sensing images (RSI) still faces significant challenges due to various spatial resolutions, cluttered backgrounds, and complex imaging conditions, mainly for two reasons: (1) accurate location of salient objects; and (2) subtle boundaries of salient objects. This paper explores the inherent properties of multi-level features to develop a novel semantic-guided attention refinement network (SARNet) for SOD of NSI. Specifically, the proposed semantic guided decoder (SGD) roughly but accurately locates the multi-scale object by aggregating multiple high-level features, and then this global semantic information guides the integration of subsequent features in a step-by-step feedback manner to make full use of deep multi-level features. Simultaneously, the proposed parallel attention fusion (PAF) module combines cross-level features and semantic-guided information to refine the object’s boundary and highlight the entire object area gradually. Finally, the proposed network architecture is trained through an end-to-end fully supervised model. Quantitative and qualitative evaluations on two public RSI datasets and additional NSI datasets across five metrics show that our SARNet is superior to 14 state-of-the-art (SOTA) methods without any post-processing.

Moving Vehicle Tracking with a Moving Drone Based on Track Association

The drone has played an important role in security and surveillance. However, due to the limited computing power and energy resources, more efficient systems are required for surveillance tasks. In this paper, we address detection and tracking of moving vehicles with a small drone. A moving object detection scheme has been developed based on frame registration and subtraction followed by morphological filtering and false alarm removing. The center position of the detected object area is the input to the tracking target as a measurement. The Kalman filter estimates the position and velocity of the target based on the measurement nearest to the state prediction. We propose a new data association scheme for multiple measurements on a single target. This track association method consists of the hypothesis testing between two tracks and track fusion through track selection and termination. We reduce redundant tracks on the same target and maintain the track with the least estimation error. In the experiment, drones flying at an altitude of 150 m captured two videos in an urban environment. There are a total of 9 and 23 moving vehicles in each video; the detection rates are 92% and 89%, respectively. The number of valid tracks is significantly reduced from 13 to 10 and 56 to 26 in the first and the second video, respectively. In the first video, the average position RMSE of two merged tracks are improved by 83.6% when only the fused states are considered. In the second video, the average position and velocity RMSE are 1.21 m and 1.97 m/s, showing the robustness of the proposed system.

Authors Identification within the Subject Area in the Semantic Library

The peculiarities of the task of authors identifying and determining author's contribution to publications in digital bibliographic codes are considered. The features of the problem of insufficient identification are manifested in the repetition of information, doubling, the presence of authors with completely coincidental names, self-quotation, autoplagiate and plagiarism itself. It is proposed to use publication information that has already been accumulated in the digital library in the form of related object area data and a variety of target thesaurus data, as the author and user of the library. This information contains links whereby keyword contexts, multiple co-authors, and term associations in dictionaries and thesauruses can be used to identify authorship. It is important that an array of scientific publications is considered, since they have an established traditional structure, which allows comparing fixed text elements (annotations, keywords, classifier codes, etc.). Thus, even if the names in the publications are fully matched, the question of authorship can be raised if the publications in the digital library correspond to different subject areas. Resolution of such contradictions is accomplished by evaluating a plurality of links of all elements of secondary publication information. The result of the comparison could be the addition of the author to a specific area, i.e. the extension of the addressee's thesaurus and the author's personal thesaurus, or the appearance of full namesakes in the library, but from different areas of knowledge. It has been shown that modern data analysis tools allow you to evaluate the author's contribution to publication, despite the fact that of course, only the scientific community can evaluate the real contribution to scientific research.

NRA-Net—Neg-Region Attention Network for Salient Object Detection with Gaze Tracking

In this paper, we propose a detection method for salient objects whose eyes are focused on gaze tracking; this method does not require a device in a single image. A network was constructed using Neg-Region Attention (NRA), which predicts objects with a concentrated line of sight using deep learning techniques. The existing deep learning-based method has an autoencoder structure, which causes feature loss during the encoding process of compressing and extracting features from the image and the decoding process of expanding and restoring. As a result, a feature loss occurs in the area of the object from the detection results, or another area is detected as an object. The proposed method, that is, NRA, can be used for reducing feature loss and emphasizing object areas with encoders. After separating positive and negative regions using the exponential linear unit activation function, converted attention was performed for each region. The attention method provided without using the backbone network emphasized the object area and suppressed the background area. In the experimental results, the proposed method showed higher detection results than the conventional methods.